Table of contents

Brain-computer interfaces (BCIs) enabling the control of a personal computer could provide myriad benefits to individuals with disabilities including paralysis. However, to realize this potential, these BCIs must gain regulatory approval and be made clinically available beyond research participation. Therefore, a transition from engineering-oriented to clinically oriented outcome measures will be required in the evaluation of BCIs. This review examined how to assess the clinical benefit of BCIs for the control of a personal computer. We report that: (a) a variety of different patient-reported outcome measures can be used to evaluate improvements in how a patient feels, and we offer some considerations that should guide instrument selection. (b) Activities of daily living can be assessed to demonstrate improvements in how a patient functions, however, new instruments that are sensitive to increases in functional independence via the ability to perform digital tasks may be needed. (c) Benefits to how a patient survives has not previously been evaluated but establishing patient-initiated communication channels using BCIs might facilitate quantifiable improvements in health outcomes.

Objective. Semantic concepts are coherent entities within our minds. They underpin our thought processes and are a part of the basis for our understanding of the world. Modern neuroscience research is increasingly exploring how individual semantic concepts are encoded within our brains and a number of studies are beginning to reveal key patterns of neural activity that underpin specific concepts. Building upon this basic understanding of the process of semantic neural encoding, neural engineers are beginning to explore tools and methods for semantic decoding: identifying which semantic concepts an individual is focused on at a given moment in time from recordings of their neural activity. In this paper we review the current literature on semantic neural decoding. Approach. We conducted this review according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. Specifically, we assess the eligibility of published peer-reviewed reports via a search of PubMed and Google Scholar. We identify a total of 74 studies in which semantic neural decoding is used to attempt to identify individual semantic concepts from neural activity. Main results. Our review reveals how modern neuroscientific tools have been developed to allow decoding of individual concepts from a range of neuroimaging modalities. We discuss specific neuroimaging methods, experimental designs, and machine learning pipelines that are employed to aid the decoding of semantic concepts. We quantify the efficacy of semantic decoders by measuring information transfer rates. We also discuss current challenges presented by this research area and present some possible solutions. Finally, we discuss some possible emerging and speculative future directions for this research area. Significance. Semantic decoding is a rapidly growing area of research. However, despite its increasingly widespread popularity and use in neuroscientific research this is the first literature review focusing on this topic across neuroimaging modalities and with a focus on quantifying the efficacy of semantic decoders.

Bioelectronic stimulation of the spinal cord has demonstrated significant progress in the restoration of motor function in spinal cord injury (SCI). The proximal, uninjured spinal cord presents a viable target for the recording and generation of control signals to drive targeted stimulation. Signals have been directly recorded from the spinal cord in behaving animals and correlated with limb kinematics. Advances in flexible materials, electrode impedance and signal analysis will allow spinal cord recording (SCR) to be used in next-generation neuroprosthetics. In this review, we summarize the technological advances enabling progress in SCR and describe systematically the clinical challenges facing spinal cord bioelectronic interfaces and potential solutions, from device manufacture, surgical implantation to chronic effects of foreign body reaction and stress–strain mismatches between electrodes and neural tissue. Finally, we establish our vision of bi-directional closed-loop spinal cord bioelectronic bypass interfaces that enable the communication of disrupted sensory signals and restoration of motor function in SCI.

Objective. Increasing complexity in extracellular stimulation experiments and neural implant design also requires realistic computer simulations capable of modeling the neural activity of nerve cells under the influence of an electrical stimulus. Classical model approaches are often based on simplifications, are not able to correctly calculate the electric field generated by complex electrode designs, and do not consider electrical effects of the cell on its surrounding. A more accurate approach is the finite element method (FEM), which provides necessary techniques to solve the Poisson equation for complex geometries under consideration of electrical tissue properties. Especially in situations where neurons experience large and non-symmetric extracellular potential gradients, a FEM solution that implements the cell membrane model can improve the computer simulation results. To investigate the response of neurons in an electric field generated by complex electrode designs, a FEM framework for extracellular stimulation was developed in COMSOL. Approach. Methods to implement morphologically- and biophysically-detailed neurons including active Hodgkin-Huxley (HH) cell membrane dynamics as well as the stimulation setup are described in detail. Covered methods are (a) development of cell and electrode geometries including meshing strategies, (b) assignment of physics for the conducting spaces and the realization of active electrodes, (c) implementation of the HH model, and (d) coupling of the physics to get a fully described model. Main results. Several implementation examples are briefly presented: (a) a full FEM implementation of a HH model cell stimulated with a honeycomb electrode, (b) the electric field of a cochlear electrode placed inside the cochlea, and (c) a proof of concept implementation of a detailed double-cable cell membrane model for myelinated nerve fibers. Significance. The presented concepts and methods provide basic and advanced techniques to realize a full FEM framework for innovative studies of neural excitation in response to extracellular stimulation.

Objective. The micro-electrode array (MEA) is a cell-culture surface with integrated electrodes used for assays of electrically excitable cells and tissues. MEAs have been a workhorse in the study of neurons and myocytes, owing to the scalability and millisecond temporal resolution of the technology. However, traditional MEAs are opaque, precluding inverted microscope access to modern genetically encoded optical sensors and effectors. Approach. To address this gap, transparent MEAs have been developed. However, for many labs, transparent MEAs remain out of reach due to the cost of commercially available products, and the complexity of custom fabrication. Here, we describe an open-source transparent MEA based on the OpenEphys platform (Siegle et al 2017 J. Neural Eng.14 045003). Main results. We demonstrate the performance of this transparent MEA in a multiplexed electrical and optogenetic assay of primary rat hippocampal neurons. Significance. This open-source transparent MEA and recording platform is designed to be accessible, requiring minimal microelectrode fabrication or circuit design experience. We include low-noise connectors for seamless integration with the Intan Technologies headstage, and a mechanically stable adaptor conforming to the 24-well plate footprint for compatibility with most inverted microscopes.

Background. Latencies of motor evoked potentials (MEPs) can provide insights into the motor neuronal pathways activated by transcranial magnetic stimulation. Notwithstanding its clinical relevance, accurate, unbiased methods to automatize latency detection are still missing. Objective. We present a novel open-source algorithm suitable for MEP onset/latency detection during resting state that only requires the post-stimulus electromyography signal and exploits the approximation of the first derivative of this signal to find the time point of initial deflection of the MEP. Approach. The algorithm has been benchmarked, using intra-class coefficient (ICC) and effect sizes, to manual detection of latencies done by three researchers independently on a dataset comprising almost 6500 MEP trials from healthy participants (n = 18) and stroke patients (n = 31) acquired during rest. The performance was further compared to currently available automatized methods, some of which created for active contraction protocols. Main results. The unstandardized effect size between the human raters and the present method is smaller than the sampling period for both healthy and pathological MEPs. Moreover, the ICC increases when the algorithm is added as a rater. Significance. The present algorithm is comparable to human expert decision and outperforms currently available methods. It provides a promising method for automated MEP latency detection under physiological and pathophysiological conditions.

Objective. Focal cortical dysplasia type IIIa (FCD IIIa) is a highly prevalent temporal lobe epilepsy but the seizure outcomes are not satisfactory after epilepsy surgery. Hence, quantitative neuroimaging, epileptogenic alterations, as well as their values in guiding surgery are worth exploring. Approach. We examined 69 patients with pathologically verified FCD IIIa using multimodal neuroimaging and stereoelectroencephalography (SEEG). Among them, 18 received postoperative imaging which showed the extent of surgical resection and 9 underwent SEEG implantation. We also explored neuroimaging gradient alterations along with the distance to the temporal pole. Subsequently, the machine learning regression model was employed to predict whole-brain epileptogenicity. Lastly, the correlation between neuroimaging or epileptogenicity and surgical cavities was assessed. Main results. FCD IIIa displayed neuroimaging gradient alterations on the temporal neocortex, morphology-signal intensity decoupling, low similarity of intra-morphological features and high similarity of intra-signal intensity features. The support vector regression model was successfully applied at the whole-brain level to calculate the continuous epileptogenic value at each vertex (mean-squared error = 13.8 ± 9.8). Significance. Our study investigated the neuroimaging gradient alterations and epileptogenicity of FCD IIIa, along with their potential values in guiding suitable resection range and in predicting postoperative seizure outcomes. The conclusions from this study may facilitate an accurate presurgical examination of FCD IIIa. However, further investigation including a larger cohort is necessary to confirm the results.

Objective. Electroencephalography is a technique for measuring normal or abnormal neuronal activity in the human brain, but its low spatial resolution makes it difficult to locate the precise locations of neurons due to the volume conduction effect of brain tissue. Approach. The acoustoelectric (AE) effect has the advantage of detecting electrical signals with high temporal resolution and focused ultrasound with high spatial resolution. In this paper, we use dipoles to simulate real single and double neurons, and further investigate the localization and decoding of single and double dipoles based on AE effects from numerical simulations, brain tissue phantom experiments, and fresh porcine brain tissue experiments. Main results. The results show that the localization error of a single dipole is less than 0.3 mm, the decoding signal is highly correlated with the source signal, and the decoding accuracy is greater than 0.94; the location of double dipoles with an interval of 0.4 mm or more can be localized, the localization error tends to increase as the interval of dipoles decreases, and the decoding accuracy tends to decrease as the frequency of dipoles decreases. Significance. This study localizes and decodes dipole signals with high accuracy, and provides a technical method for the development of EEG.

Objective. Brain recordings exhibit dynamics at multiple spatiotemporal scales, which are measured with spike trains and larger-scale field potential signals. To study neural processes, it is important to identify and model causal interactions not only at a single scale of activity, but also across multiple scales, i.e. between spike trains and field potential signals. Standard causality measures are not directly applicable here because spike trains are binary-valued but field potentials are continuous-valued. It is thus important to develop computational tools to recover multiscale neural causality during behavior, assess their performance on neural datasets, and study whether modeling multiscale causalities can improve the prediction of neural signals beyond what is possible with single-scale causality. Approach. We design a multiscale model-based Granger-like causality method based on directed information and evaluate its success both in realistic biophysical spike-field simulations and in motor cortical datasets from two non-human primates (NHP) performing a motor behavior. To compute multiscale causality, we learn point-process generalized linear models that predict the spike events at a given time based on the history of both spike trains and field potential signals. We also learn linear Gaussian models that predict the field potential signals at a given time based on their own history as well as either the history of binary spike events or that of latent firing rates. Main results. We find that our method reveals the true multiscale causality network structure in biophysical simulations despite the presence of model mismatch. Further, models with the identified multiscale causalities in the NHP neural datasets lead to better prediction of both spike trains and field potential signals compared to just modeling single-scale causalities. Finally, we find that latent firing rates are better predictors of field potential signals compared with the binary spike events in the NHP datasets. Significance. This multiscale causality method can reveal the directed functional interactions across spatiotemporal scales of brain activity to inform basic science investigations and neurotechnologies.

Objective. One promising approach towards further improving cochlear implants (CI) is to use brain signals controlling the device in order to close the auditory loop. Initial electroencephalography (EEG) studies have already shown promising results. However, they are based on noninvasive measurements, whereas implanted electrodes are expected to be more convenient in terms of everyday-life usability. If additional measurement electrodes were implanted during CI surgery, then invasive recordings should be possible. Furthermore, implantation will provide better signal quality, higher robustness to artefacts, and thus enhanced classification accuracy. Approach. In an initial project, three additional epidural electrodes were temporarily implanted during the surgical procedure. After surgery, different auditory evoked potentials (AEPs) were recorded both invasively (epidural) and using surface electrodes, with invasively recorded signals demonstrated as being markedly superior. In this present analysis, cortical evoked response audiometry (CERA) signals recorded in seven patients were used for single-trial classification of sounds with different intensities. For classification purposes, we used shrinkage-regularized linear discriminant analysis (sLDA). Clinical speech perception scores were also investigated. Main results. Analysis of CERA data from different subjects showed single-trial classification accuracies of up to 99.2% for perceived vs. non-perceived sounds. Accuracies of up to 89.1% were achieved in classification of sounds perceived at different intensities. Highest classification accuracies were achieved by means of epidural recordings. Required loudness differences seemed to correspond to speech perception in noise. Significance. The proposed epidural recording approach showed good classification accuracy into sound perceived and not perceived when the best-performing electrodes were selected. Classifying different levels of sound stimulation accurately proved more challenging. At present, the methods explored in this study would not be sufficiently reliable to allow automated closed-loop control of CI parameters. However, our findings are an important initial contribution towards improving applicability of closed auditory loops and for next-generation automatic fitting approaches.

Objective. Metal implants impact the dosimetry assessment in electrical stimulation techniques. Therefore, they need to be included in numerical models. While currents in the body are ionic, metals only allow electron transport. In fact, charge transfer between tissues and metals requires electric fields to drive electrochemical reactions at the interface. Thus, metal implants may act as insulators or as conductors depending on the scenario. The aim of this paper is to provide a theoretical argument that guides the choice of the correct representation of metal implants in electrical models while considering the electrochemical nature of the problem Approach. We built a simple model of a metal implant exposed to a homogeneous electric field of various magnitudes. The same geometry was solved using two different models: a purely electric one (with different conductivities for the implant), and an electrochemical one. As an example of application, we also modeled a transcranial electrical stimulation (tES) treatment in a realistic head model with a skull plate using a high and low conductivity value for the plate. Main results. Metal implants generally act as electric insulators when exposed to electric fields up to around 100 V m⁻¹ and they only resemble a perfect conductor for fields in the order of 1000 V m⁻¹ and above. The results are independent of the implant's metal, but they depend on its geometry. tES modeling with implants incorrectly treated as conductors can lead to errors of 50% or more in the estimation of the induced fields Significance. Metal implants can be accurately represented by a simple electrical model of constant conductivity, but an incorrect model choice can lead to large errors in the dosimetry assessment. Our results can be used to guide the selection of the most appropriate model in each scenario.

Objective. The central-to-peripheral voluntary motor effort (VME) in the affected limb is a dominant force for driving the functional neuroplasticity on motor restoration post-stroke. However, current rehabilitation robots isolated the central and peripheral involvements in the control design, resulting in limited rehabilitation effectiveness. This study was to design a corticomuscular coherence (CMC) and electromyography (EMG)-driven control to integrate the central and peripheral VMEs in neuromuscular systems in stroke survivors. Approach. The CMC-EMG-driven control was developed in a neuromuscular electrical stimulation (NMES)-robot system, i.e. CMC-EMG-driven NMES-robot system, to instruct and assist the wrist-hand extension and flexion in persons after stroke. A pilot single-group trial of 20 training sessions was conducted with the developed system to assess the feasibility for wrist-hand practice on the chronic strokes (16 subjects). The rehabilitation effectiveness was evaluated through clinical assessments, CMC, and EMG activation levels. Main results. The trigger success rate and laterality index of CMC were significantly increased in wrist-hand extension across training sessions (p < 0.05). After the training, significant improvements in the target wrist-hand joints and suppressed compensation from the proximal shoulder-elbow joints were observed through the clinical scores and EMG activation levels (p < 0.05). The central-to-peripheral VME distribution across upper extremity (UE) muscles was also significantly improved, as revealed by the CMC values (p < 0.05). Significance. Precise wrist-hand rehabilitation was achieved by the developed system, presenting suppressed cortical and muscular compensation from the contralesional hemisphere and the proximal UE, and improved distribution of the central-and-peripheral VME on UE muscles. ClinicalTrials.gov Register Number NCT02117089

Objective. The purpose of this study is to localize the seizure onset zone of patients suffering from drug-resistant epilepsy. During the last two decades, multiple studies proposed the use of independent component analysis (ICA) to analyze ictal electroencephalogram (EEG) recordings. This study aims at evaluating ICA potential with quantitative measurements. In particular, we address the challenging step where the components extracted by ICA of an ictal nature must be selected. Approach. We considered a cohort of 10 patients suffering from extratemporal lobe epilepsy who were rendered seizure-free after surgery. Different sets of pre-processing parameters were compared and component features were explored to help distinguish ictal components from others. Quantitative measurements were implemented to determine whether some of the components returned by ICA were located within the resection zone and thus likely to be ictal. Finally, an assistance to the component selection was proposed based on the implemented features. Main results. For every seizure, at least one component returned by ICA was localized within the resection zone, with the optimal pre-processing parameters. Three features were found to distinguish components localized within the resection zone: the dispersion of their active brain sources, the ictal rhythm power and the contribution to the EEG variance. Using the implemented component selection assistance based on the features, the probability that the first proposed component yields an accurate estimation reaches 51.43% (without assistance: 24.74%). The accuracy reaches 80% when considering the best result within the first five components. Significance. This study confirms the utility of ICA for ictal EEG analysis in extratemporal lobe epilepsy, and suggests relevant features to analyze the components returned by ICA. A component selection assistance is proposed to guide clinicians in their choice for ictal components.

Objective. Targeted electrical stimulation of the brain perturbs neural networks and modulates their rhythmic activity both at the site of stimulation and at remote brain regions. Understanding, or even predicting, this neuromodulatory effect is crucial for any therapeutic use of brain stimulation. The objective of this study was to investigate if brain network properties prior to stimulation sessions hold associative and predictive value in understanding the neuromodulatory effect of electrical stimulation in a clinical context. Approach. We analysed the stimulation responses in 131 stimulation sessions across 66 patients with focal epilepsy recorded through intracranial electroencephalogram (iEEG). We considered functional and structural connectivity features as predictors of the response at every iEEG contact. Taking advantage of multiple recordings over days, we also investigated how slow changes in interictal functional connectivity (FC) ahead of the stimulation, representing the long-term variability of FC, relate to stimulation responses. Main results. The long-term variability of FC exhibits strong association with the stimulation-induced increases in delta and theta band power. Furthermore, we show through cross-validation that long-term variability of FC improves prediction of responses above the performance of spatial predictors alone. Significance. This study highlights the importance of the slow dynamics of FC in the prediction of brain stimulation responses. Furthermore, these findings can enhance the patient-specific design of effective neuromodulatory protocols for therapeutic interventions.

Objective. Cognitive impairment is one of the core symptoms of schizophrenia, with an emphasis on dysfunctional information processing. Sensory gating deficits have consistently been reported in schizophrenia, but the underlying physiological mechanism is not well-understood. We report the discovery and characterization of P50 dynamic brain connections based on microstate analysis. Approach. We identify five main microstates associated with the P50 response and the difference between the first and second click presentation (S1-S2-P50) in first-episode schizophrenia (FESZ) patients, ultra-high-risk individuals (UHR) and healthy controls (HCs). We used the signal segments composed of consecutive time points with the same microstate label to construct brain functional networks. Main results. The microstate with a prefrontal extreme location during the response to the S1 of P50 are statistically different in duration, occurrence and coverage among the FESZ, UHR and HC groups. In addition, a microstate with anterior–posterior orientation was found to be associated with S1-S2-P50 and its coverage was found to differ among the FESZ, UHR and HC groups. Source location of microstates showed that activated brain regions were mainly concentrated in the right temporal lobe. Furthermore, the connectivities between brain regions involved in P50 processing of HC were widely different from those of FESZ and UHR. Significance. Our results indicate that P50 suppression deficits in schizophrenia may be due to both aberrant baseline sensory perception and adaptation to repeated stimulus. Our findings provide new insight into the mechanisms of P50 suppression in the early stage of schizophrenia.

Objective. This article presents a novel transcranial magnetic stimulation (TMS) pulse generator with a wide range of pulse shape, amplitude, and width. Approach. Based on a modular multilevel TMS (MM-TMS) topology we had proposed previously, we realized the first such device operating at full TMS energy levels. It consists of ten cascaded H-bridge modules, each implemented with insulated-gate bipolar transistors, enabling both novel high-amplitude ultrabrief pulses as well as pulses with conventional amplitude and duration. The MM-TMS device can output pulses including up to 21 voltage levels with a step size of up to 1100 V, allowing relatively flexible generation of various pulse waveforms and sequences. The circuit further allows charging the energy storage capacitor on each of the ten cascaded modules with a conventional TMS power supply. Main results. The MM-TMS device can output peak coil voltages and currents of 11 kV and 10 kA, respectively, enabling suprathreshold ultrabrief pulses (>8.25 μs active electric field phase). Further, the MM-TMS device can generate a wide range of near-rectangular monophasic and biphasic pulses, as well as more complex staircase-approximated sinusoidal, polyphasic, and amplitude-modulated pulses. At matched estimated stimulation strength, briefer pulses emit less sound, which could enable quieter TMS. Finally, the MM-TMS device can instantaneously increase or decrease the amplitude from one pulse to the next in discrete steps by adding or removing modules in series, which enables rapid pulse sequences and paired-pulse protocols with variable pulse shapes and amplitudes. Significance. The MM-TMS device allows unprecedented control of the pulse characteristics which could enable novel protocols and quieter pulses.

Objective. The objectives of this study were to assess gait biomechanics and the effect of overground walking speed on gait parameters, kinematics, and electromyographic (EMG) activity in the hindlimb muscles of Yucatan minipigs (YMPs). Approach. Nine neurologically-intact, adult YMPs were trained to walk overground in a straight line. Whole-body kinematics and EMG activity of hindlimb muscles were recorded and analyzed at six different speed ranges (0.4–0.59, 0.6–0.79, 0.8–0.99, 1.0–1.19, 1.2–1.39, and 1.4–1.6 m s⁻¹). A MATLAB program was developed to detect strides and gait events automatically from motion-captured data. The kinematics and EMG activity were analyzed for each stride based on the detected events. Main results. Significant decreases in stride duration, stance and swing times and an increase in stride length were observed with increasing speed. A transition in gait pattern occurred at the 1.0 m s⁻¹ walking speed. Significant increases in the range of motion of the knee and ankle joints were observed at higher speeds. Also, the points of minimum and maximum joint angles occurred earlier in the gait cycle as the walking speed increased. The onset of EMG activity in the biceps femoris muscle occurred significantly earlier in the gait cycle with increasing speed. Significance. YMPs are becoming frequently used as large animal models for preclinical testing and translation of novel interventions to humans. A comprehensive characterization of overground walking in neurologically-intact YMPs is provided in this study. These normative measures set the basis against which the effects of future interventions on locomotor capacity in YMPs can be compared.

Objective. Brachial plexus injuries (BPIs) result in serious dysfunction, especially brachial plexus defects which are currently treated using autologous nerve graft (autograft) transplantation. With the development of tissue engineering, tissue engineered nerve grafts (TENGs) have emerged as promising alternatives to autografts but have not yet been widely applied to the treatment of BPIs. Herein, we developed a TENG modified with extracellular matrix generated by skin-derived precursor Schwann cells (SKP-SCs) and expand its application in upper brachial plexus defects in rats. Approach. SKP-SCs were co-cultured with chitosan neural conduits or silk fibres and subjected to decellularization treatment. Ten bundles of silk fibres (five fibres per bundle) were placed into a conduit to obtain the TENG, which was used to bridge an 8 mm gap in the upper brachial plexus. The efficacy of this treatment was examined for TENG-, autograft- and scaffold-treated groups at several times after surgery using immunochemical staining, behavioural tests, electrophysiological measurements, and electron microscopy. Main results. Histological analysis conducted two weeks after surgery showed that compared to scaffold bridging, TENG treatment enhanced the growth of regenerating axons. Behavioural tests conducted four weeks after surgery showed that TENG-treated rats performed similarly to autograft-treated ones, with a significant improvement observed in both cases compared with the scaffold treatment group. Electrophysiological and retrograde tracing characterizations revealed that the target muscles were reinnervated in both TENG and autograft groups, while transmission electron microscopy and immunohistochemical staining showed the occurrence of the superior myelination of regenerated axons in these groups. Significance. Treatment with the developed TENG allows the effective bridging of proximal nerve defects in the upper extremities, and the obtained results provide a theoretical basis for clinical transformation to expand the application scope of TENGs.

Objective. Research of surface electromyography (sEMG) signal modeling and feature extraction is important in human motion intention recognition, prosthesis and exoskeleton robots. However, the existing methods mostly use the signal segmentation processing method rather than the point-to-point signal processing method, and lack physiological mechanism support. Approach. In this study, a real-time sEMG signal modeling and separation method is developed based on oscillatory theory. On this basis, an sEMG signal feature extraction method is constructed, and an ensemble learning method is combined to achieve real-time human hand motion intention recognition. Main results. The experimental results show that the average root mean square difference value of the sEMG signal modeling is 0.3838 ± 0.0591, and the average accuracy of human hand motion intention recognition is 96.03 ± 1.74%. On a computer with Intel (R) Core (TM) i5-8250U CPU running Matlab 2016Rb, the execution time for the sEMG signal with an actual duration of 2 s is 0.66 s. Significance. Compared with several existing methods, the proposed method has better modeling accuracy, motion intention recognition accuracy and real-time performance. The method developed in this study may provide a new perspective on sEMG modeling and feature extraction for hand movement classification.

Objective. Cultures have essential influences on emotions. However, most studies on cultural influences on emotions are in the areas of psychology and neuroscience, while the existing affective models are mostly built with data from the same culture. In this paper, we identify the similarities and differences among Chinese, German, and French individuals in emotion recognition with electroencephalogram (EEG) and eye movements from an affective computing perspective. Approach. Three experimental settings were designed: intraculture subject dependent, intraculture subject independent, and cross-culture subject independent. EEG and eye movements are acquired simultaneously from Chinese, German, and French subjects while watching positive, neutral, and negative movie clips. The affective models for Chinese, German, and French subjects are constructed by using machine learning algorithms. A systematic analysis is performed from four aspects: affective model performance, neural patterns, complementary information from different modalities, and cross-cultural emotion recognition. Main results. From emotion recognition accuracies, we find that EEG and eye movements can adapt to Chinese, German, and French cultural diversities and that a cultural in-group advantage phenomenon does exist in emotion recognition with EEG. From the topomaps of EEG, we find that the γ and β bands exhibit decreasing activities for Chinese, while for German and French, θ and α bands exhibit increasing activities. From confusion matrices and attentional weights, we find that EEG and eye movements have complementary characteristics. From a cross-cultural emotion recognition perspective, we observe that German and French people share more similarities in topographical patterns and attentional weight distributions than Chinese people while the data from Chinese are a good fit for test data but not suitable for training data for the other two cultures. Significance. Our experimental results provide concrete evidence of the in-group advantage phenomenon, cultural influences on emotion recognition, and different neural patterns among Chinese, German, and French individuals.

Objective. Choosing the optimal electrode trajectory, stimulation location, and stimulation amplitude in subthalamic nucleus deep brain stimulation (STN DBS) for Parkinson's disease remains a time-consuming empirical effort. In this retrospective study, we derive a data-driven electrophysiological biomarker that predicts clinical DBS location and parameters, and we consolidate this information into a quantitative score that may facilitate an objective approach to STN DBS surgery and programming. Approach. Random-forest feature selection was applied to a dataset of 1046 microelectrode recordings (MERs) sites across 20 DBS implant trajectories to identify features of oscillatory activity that predict clinically programmed volumes of tissue activation (VTAs). A cross-validated classifier was used to retrospectively predict VTA regions from these features. Spatial convolution of probabilistic classifier outputs along MER trajectories produced a biomarker score that reflects the probability of localization within a clinically optimized VTA. Main results. Biomarker scores peaked within the VTA region and were significantly correlated with percent improvement in postoperative motor symptoms (Part III of the Movement Disorders Society revision of the Unified Parkinson Disease Rating Scale, R = 0.61, p = 0.004). Notably, the length of STN, a common criterion for trajectory selection, did not show similar correlation (R = −0.31, p = 0.18). These findings suggest that biomarker-based trajectory selection and programming may improve motor outcomes by 9 ± 3 percentage points (p = 0.047) in this dataset. Significance. A clinically defined electrophysiological biomarker not only predicts VTA size and location but also correlates well with motor outcomes. Use of this biomarker for trajectory selection and initial stimulation may potentially simplify STN DBS surgery and programming.

Objective. Transcranial ultrasound stimulation (TUS), a large penetration depth and high spatial resolution technology, has developed rapidly in recent years. This study aimed to explore and evaluate the neuromodulation effects of TUS on mouse motor neural circuits under different parameters. Approach. Our study used functional corticomuscular coupling (FCMC) as an index to explore the modulation mechanism for movement control under different TUS parameters (intensity [${I_{{\text{sppa}}}}$] and stimulation duration). We collected local field potential (LFP) and tail electromyographic (EMG) data under TUS in healthy mice and then introduced the time-frequency coherence method to analyze the FCMC before and after TUS in the time-frequency domain. After that, we defined the relative coherence area to quantify the coherence between LFP and EMG under TUS. Main results. The FCMC at theta, alpha, beta, and gamma bands was enhanced after TUS, and the neuromodulation efficacy mainly occurred in the lower frequency band (theta and alpha band). After TUS with different parameters, the FCMC in all selected frequency bands showed a tendency of increasing first and then decreasing. Further analysis showed that the maximum coupling value of theta band appeared from 0.2 to 0.4 s, and that the maximum coupling value in alpha and gamma band appeared from 0 to 0.2 s. Significance. The aforementioned results demonstrate that FCMC in the motor cortex could be modulated by TUS. We provide a theoretical basis for further exploring the modulation mechanism of TUS parameters and clinical application.

Objective. We present a combination of a power electronics system and magnetic nanoparticles that enable frequency-multiplexed magnetothermal-neurostimulation with rapid channel switching between three independent channels spanning a wide frequency range. Approach. The electronics system generates alternating magnetic field spanning 50 kHz to 5 MHz in the same coil by combining silicon (Si) and gallium-nitride (GaN) transistors to resolve the high spread of coil impedance and current required throughout the wide bandwidth. The system drives a liquid-cooled field coil via capacitor banks, forming three series resonance channels which are multiplexed using high-voltage contactors. We characterized the system by the output channels' frequencies, field strength, and switching time, as well as the system's overall operation stability. Using different frequency–amplitude combinations of the magnetic field to target specific magnetic nanoparticles with different coercivity, we demonstrate actuation of iron oxide nanoparticles in all three channels, including a novel nanoparticle composition responding to magnetic fields in the megahertz range. Main results. The system achieved the desired target field strengths for three frequency channels, with switching speed between channels on the order of milliseconds. Specific absorption rate measurements and infrared thermal imaging performed with three types of magnetic nanoparticles demonstrated selective heating and validated the system's intended use. Significance. The system uses a hybrid of Si and GaN transistors in bridge configuration instead of conventional amplifier circuit concepts to drive the magnetic field coil and contactors for fast switching between different capacitor banks. Series-resonance circuits ensure a high output quality while keeping the system efficient. This approach could significantly improve the speed and flexibility of frequency-multiplexed nanoparticle actuation, such as magnetogenetic neurostimulation, and thus provide the technical means for selective stimulation below the magnetic field's fundamental spatial focality limits.

Objective. Temporal resolution is a key challenge in artificial vision. Several prosthetic approaches are limited by the perceptual fading of evoked phosphenes upon repeated stimulation from the same electrode. Therefore, implanted patients are forced to perform active scanning, via head movements, to refresh the visual field viewed by the camera. However, active scanning is a draining task, and it is crucial to find compensatory strategies to reduce it. Approach. To address this question, we implemented perceptual fading in simulated prosthetic vision using virtual reality. Then, we quantified the effect of fading on two indicators: the time to complete a reading task and the head rotation during the task. We also tested if stimulation strategies previously proposed to increase the persistence of responses in retinal ganglion cells to electrical stimulation could improve these indicators. Main results. This study shows that stimulation strategies based on interrupted pulse trains and randomisation of the pulse duration allows significant reduction of both the time to complete the task and the head rotation during the task. Significance. The stimulation strategy used in retinal implants is crucial to counteract perceptual fading and to reduce active head scanning during prosthetic vision. In turn, less active scanning might improve the patient's comfort in artificial vision.

Background. Transcutaneous electrical nerve stimulation (TENS) is generally applied for tactile feedback in the field of prosthetics. The distinct mechanisms of evoked tactile perception between stimulus patterns in conventional TENS (cTENS) and neuromorphic TENS (nTENS) are relatively unknown. This is the first study to investigate the neurobiological effect of nTENS for cortical functional mechanism in evoked tactile perception. Methods. Twenty-one healthy participants were recruited in this study. Electroencephalogram (EEG) was recorded while the participants underwent a tactile discrimination task. One cTENS pattern (square pattern) and two nTENS patterns (electromyography and single motor unit patterns) were applied to evoke tactile perception in four fingers, including the right and left index and little fingers. EEG was preprocessed and somatosensory-evoked potentials (SEPs) were determined. Then, source-level functional networks based on graph theory were evaluated, including clustering coefficient, path length, global efficiency, and local efficiency in six frequency bands. Main results. Behavioral results suggested that the single motor units (SMUs) pattern of nTENS was the most natural tactile perception. SEPs results revealed that SMU pattern exhibited significant shorter latency in P1 and N1 components than the other patterns, while nTENS patterns have significantly longer latency in P3 component than cTENS pattern. Cortical functional networks showed that the SMU pattern had the lowest short path and highest efficiency in beta and gamma bands. Conclusion. This study highlighted that distinct TENS patterns could affect brain activities. The new characteristics in tactile manifestation of nTENS would provide insights for the application of tactile perception restoration.

Objective. Visual outcomes provided by present retinal prostheses that primarily target retinal ganglion cells (RGCs) through epiretinal stimulation remain rudimentary, partly due to the limited knowledge of retinal responses under electrical stimulation. Better understanding of how different retinal regions can be quantitatively controlled with high spatial accuracy, will be beneficial to the design of micro-electrode arrays and stimulation strategies for next-generation wide-view, high-resolution epiretinal implants. Approach. A computational model was developed to assess neural activity at different eccentricities (2 mm and 5 mm) within the human retina. This model included midget and parasol RGCs with anatomically accurate cell distribution and cell-specific morphological information. We then performed in silico investigations of region-specific RGC responses to epiretinal electrical stimulation using varied electrode sizes (5–210 µm diameter), emulating both commercialized retinal implants and recently developed prototype devices. Main results. Our model of epiretinal stimulation predicted RGC population excitation analogous to the complex percepts reported in human subjects. Following this, our simulations suggest that midget and parasol RGCs have characteristic regional differences in excitation under preferred electrode sizes. Relatively central (2 mm) regions demonstrated higher number of excited RGCs but lower overall activated receptive field (RF) areas under the same stimulus amplitudes (two-way analysis of variance (ANOVA), p < 0.05). Furthermore, the activated RGC numbers per unit active RF area (number-RF ratio) were significantly higher in central than in peripheral regions, and higher in the midget than in the parasol population under all tested electrode sizes (two-way ANOVA, p < 0.05). Our simulations also suggested that smaller electrodes exhibit a higher range of controllable stimulation parameters to achieve pre-defined performance of RGC excitation. An empirical model: I = a · exp (b · D) + c of the stimulus amplitude (I)–electrode diameter (D) relationship was constructed to achieve the pre-defined objective function values in different retinal regions, indicating the ability of controlling retinal outputs by fine-tuning the stimulation amplitude with different electrode sizes. Finally, our multielectrode simulations predicted differential neural crosstalk between adjacent electrodes in central temporal and peripheral temporal regions, providing insights towards establishing a non-uniformly distributed multielectrode array geometry for wide-view retinal implants. Significance. Stimulus–response properties in central and peripheral retina can provide useful information to estimate electrode parameters for region-specific activation by retinal stimulation. Our findings support the possibility of improving the performance of epiretinal prostheses by exploring the influence of electrode array geometry on activation of different retinal regions.

Objective. Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface offers a promising way to improve the efficiency of motor rehabilitation and motor skill learning. In recent years, the power of dynamic network analysis for MI classification has been proved. In fact, its usability mainly depends on the accurate estimation of brain connection. However, traditional dynamic network estimation strategies such as adaptive directed transfer function (ADTF) are designed in the L2-norm. Usually, they estimate a series of pseudo connections caused by outliers, which results in biased features and further limits its online application. Thus, how to accurately infer dynamic causal relationship under outlier influence is urgent. Approach. In this work, we proposed a novel ADTF, which solves the dynamic system in the L1-norm space (L1-ADTF), so as to restrict the outlier influence. To enhance its convergence, we designed an iteration strategy with the alternating direction method of multipliers, which could be used for the solution of the dynamic state-space model restricted in the L1-norm space. Furthermore, we compared L1-ADTF to traditional ADTF and its dual extension across both simulation and real EEG experiments. Main results. A quantitative comparison between L1-ADTF and other ADTFs in simulation studies demonstrates that fewer bias errors and more desirable dynamic state transformation patterns can be captured by the L1-ADTF. Application to real MI EEG datasets seriously noised by ocular artifacts also reveals the efficiency of the proposed L1-ADTF approach to extract the time-varying brain neural network patterns, even when more complex noises are involved. Significance. The L1-ADTF may not only be capable of tracking time-varying brain network state drifts robustly but may also be useful in solving a wide range of dynamic systems such as trajectory tracking problems and dynamic neural networks.

Objective. Transcutaneous electrical nerve stimulation (TENS) has been suggested as a possible non-invasive pain treatment. However, the underlying mechanism of the analgesic effect of TENS and how brain network functional connectivity (FC) is affected following the use of TENS is not yet fully understood. The purpose of this study was to investigate the effect of high-frequency TENS on the alteration of functional brain network connectivity and the corresponding topographical changes, besides perceived sensations. Approach. Forty healthy subjects participated in this study. Electroencephalography (EEG) data and sensory profiles were recorded before and up to an hour following high-frequency TENS (100 Hz) in sham and intervention groups. Brain source activity from EEG data was estimated using the LORETA algorithm. In order to generate the functional brain connectivity network, the Phase Lag Index was calculated for all pair-wise connections of eight selected brain areas over six different frequency bands (i.e. δ, θ, α, β, γ, and 0.5–90 Hz). Main results. The results suggested that the FC between the primary somatosensory cortex (SI) and the anterior cingulate cortex, in addition to FC between SI and the medial prefrontal cortex, were significantly increased in the gamma-band, following the TENS intervention. Additionally, using graph theory, several significant changes were observed in global and local characteristics of functional brain connectivity in gamma-band. Significance. Our observations in this paper open a neuropsychological window of understanding the underlying mechanism of TENS and the corresponding changes in functional brain connectivity, simultaneously with alteration in sensory perception.

Objective. The article aims at addressing 2 challenges to step motor brain-computer interface (BCI) out of laboratories: asynchronous control of complex bimanual effectors with large numbers of degrees of freedom, using chronic and safe recorders, and the decoding performance stability over time without frequent decoder recalibration. Approach. Closed-loop adaptive/incremental decoder training is one strategy to create a model stable over time. Adaptive decoders update their parameters with new incoming data, optimizing the model parameters in real time. It allows cross-session training with multiple recording conditions during closed loop BCI experiments. In the article, an adaptive tensor-based recursive exponentially weighted Markov-switching multi-linear model (REW-MSLM) decoder is proposed. REW-MSLM uses a mixture of expert (ME) architecture, mixing or switching independent decoders (experts) according to the probability estimated by a 'gating' model. A Hidden Markov model approach is employed as gating model to improve the decoding robustness and to provide strong idle state support. The ME architecture fits the multi-limb paradigm associating an expert to a particular limb or action. Main results. Asynchronous control of an exoskeleton by a tetraplegic patient using a chronically implanted epidural electrocorticography (EpiCoG) recorder is reported. The stable over a period of six months (without decoder recalibration) eight-dimensional alternative bimanual control of the exoskeleton and its virtual avatar is demonstrated. Significance. Based on the long-term (>36 months) chronic bilateral EpiCoG recordings in a tetraplegic (ClinicalTrials.gov, NCT02550522), we addressed the poorly explored field of asynchronous bimanual BCI. The new decoder was designed to meet to several challenges: the high-dimensional control of a complex effector in experiments closer to real-world behavior (point-to-point pursuit versus conventional center-out tasks), with the ability of the BCI system to act as a stand-alone device switching between idle and control states, and a stable performance over a long period of time without decoder recalibration.

Objective. Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation method that is used to study brain function and conduct neuropsychiatric therapy. Computational methods that are commonly used for electric field (E-field) dosimetry of TMS are limited in accuracy and precision because of possible geometric errors introduced in the generation of head models by segmenting medical images into tissue types. This paper studies E-field prediction fidelity as a function of segmentation accuracy. Approach.The errors in the segmentation of medical images into tissue types are modeled as geometric uncertainty in the shape of the boundary between tissue types. For each tissue boundary realization, we then use an in-house boundary element method to perform a forward propagation analysis and quantify the impact of tissue boundary uncertainties on the induced cortical E-field. Main results. Our results indicate that predictions of E-field induced in the brain are negligibly sensitive to segmentation errors in scalp, skull and white matter (WM), compartments. In contrast, E-field predictions are highly sensitive to possible cerebrospinal fluid (CSF) segmentation errors. Specifically, the segmentation errors on the CSF and gray matter interface lead to higher E-field uncertainties in the gyral crowns, and the segmentation errors on CSF and WM interface lead to higher uncertainties in the sulci. Furthermore, the uncertainty of the average cortical E-fields over a region exhibits lower uncertainty relative to point-wise estimates. Significance. The accuracy of current cortical E-field simulations is limited by the accuracy of CSF segmentation accuracy. Other quantities of interest like the average of the E-field over a cortical region could provide a dose quantity that is robust to possible segmentation errors.

Objective. Motor brain-computer interfaces (BCIs) are a promising technology that may enable motor-impaired people to interact with their environment. BCIs would potentially compensate for arm and hand function loss, which is the top priority for individuals with tetraplegia. Designing real-time and accurate BCI is crucial to make such devices useful, safe, and easy to use by patients in a real-life environment. Electrocorticography (ECoG)-based BCIs emerge as a good compromise between invasiveness of the recording device and good spatial and temporal resolution of the recorded signal. However, most ECoG signal decoders used to predict continuous hand movements are linear models. These models have a limited representational capacity and may fail to capture the relationship between ECoG signal features and continuous hand movements. Deep learning (DL) models, which are state-of-the-art in many problems, could be a solution to better capture this relationship. Approach. In this study, we tested several DL-based architectures to predict imagined 3D continuous hand translation using time-frequency features extracted from ECoG signals. The dataset used in the analysis is a part of a long-term clinical trial (ClinicalTrials.gov identifier: NCT02550522) and was acquired during a closed-loop experiment with a tetraplegic subject. The proposed architectures include multilayer perceptron, convolutional neural networks (CNNs), and long short-term memory networks (LSTM). The accuracy of the DL-based and multilinear models was compared offline using cosine similarity. Main results. Our results show that CNN-based architectures outperform the current state-of-the-art multilinear model. The best architecture exploited the spatial correlation between neighboring electrodes with CNN and benefited from the sequential character of the desired hand trajectory by using LSTMs. Overall, DL increased the average cosine similarity, compared to the multilinear model, by up to 60%, from 0.189 to 0.302 and from 0.157 to 0.249 for the left and right hand, respectively. Significance. This study shows that DL-based models could increase the accuracy of BCI systems in the case of 3D hand translation prediction in a tetraplegic subject.

Objective. Neural prosthetics often use intracortical microstimulation (ICMS) for sensory restoration. To restore natural and functional feedback, we must first understand how stimulation parameters influence the recruitment of neural populations. ICMS waveform asymmetry modulates the spatial activation of neurons around an electrode at 10 Hz; however, it is unclear how asymmetry may differentially modulate population activity at frequencies typically employed in the clinic (e.g. 100 Hz). We hypothesized that stimulation waveform asymmetry would differentially modulate preferential activation of certain neural populations, and the differential population activity would be frequency-dependent. Approach. We quantified how asymmetric stimulation waveforms delivered at 10 or 100 Hz for 30 s modulated spatiotemporal activity of cortical layer II/III pyramidal neurons using in vivo two-photon and mesoscale calcium imaging in anesthetized mice. Asymmetry is defined in terms of the ratio of the duration of the leading phase to the duration of the return phase of charge-balanced cathodal- and anodal-first waveforms (i.e. longer leading phase relative to return has larger asymmetry). Main results. Neurons within 40–60 µm of the electrode display stable stimulation-induced activity indicative of direct activation, which was independent of waveform asymmetry. The stability of 72% of activated neurons and the preferential activation of 20%–90% of neurons depended on waveform asymmetry. Additionally, this asymmetry-dependent activation of different neural populations was associated with differential progression of population activity. Specifically, neural activity tended to increase over time during 10 Hz stimulation for some waveforms, whereas activity remained at the same level throughout stimulation for other waveforms. During 100 Hz stimulation, neural activity decreased over time for all waveforms, but decreased more for the waveforms that resulted in increasing neural activity during 10 Hz stimulation. Significance. These data demonstrate that at frequencies commonly used for sensory restoration, stimulation waveform alters the pattern of activation of different but overlapping populations of excitatory neurons. The impact of these waveform specific responses on the activation of different subtypes of neurons as well as sensory perception merits further investigation.

Objective. To provide a design analysis and guidance framework for the implementation of concurrent stimulation and sensing during adaptive deep brain stimulation (aDBS) with particular emphasis on artifact mitigations. Approach. We defined a general architecture of feedback-enabled devices, identified key components in the signal chain which might result in unwanted artifacts and proposed methods that might ultimately enable improved aDBS therapies. We gathered data from research subjects chronically-implanted with an investigational aDBS system, Summit RC + S, to characterize and explore artifact mitigations arising from concurrent stimulation and sensing. We then used a prototype investigational implantable device, DyNeuMo, and a bench-setup that accounts for tissue–electrode properties, to confirm our observations and verify mitigations. The strategies to reduce transient stimulation artifacts and improve performance during aDBS were confirmed in a chronic implant using updated configuration settings. Main results. We derived and validated a 'checklist' of configuration settings to improve system performance and areas for future device improvement. Key considerations for the configuration include (a) active instead of passive recharge, (b) sense-channel blanking in the amplifier, (c) high-pass filter settings, (d) tissue–electrode impedance mismatch management, (e) time-frequency trade-offs in the classifier, (f) algorithm blanking and transition rate limits. Without proper channel configuration, the aDBS algorithm was susceptible to limit-cycles of oscillating stimulation independent of physiological state. By applying the checklist, we could optimize each block's performance characteristics within the overall system. With system-level optimization, a 'fast' aDBS prototype algorithm was demonstrated to be feasible without reentrant loops, and with noise performance suitable for subcortical brain circuits. Significance. We present a framework to study sources and propose mitigations of artifacts in devices that provide chronic aDBS. This work highlights the trade-offs in performance as novel sensing devices translate to the clinic. Finding the appropriate balance of constraints is imperative for successful translation of aDBS therapies.

Clinical trial: Institutional Review Board and Investigational Device Exemption numbers: NCT02649166/IRB201501021 (University of Florida), NCT04043403/IRB52548 (Stanford University), NCT03582891/IRB1824454 (University of California San Francisco). IDE #180 097.

Objective: Neurons communicate with each other by sending action potentials (APs) through their axons. The velocity of axonal signal propagation describes how fast electrical APs can travel. This velocity can be affected in a human brain by several pathologies, including multiple sclerosis, traumatic brain injury and channelopathies. High-density microelectrode arrays (HD-MEAs) provide unprecedented spatio-temporal resolution to extracellularly record neural electrical activity. The high density of the recording electrodes enables to image the activity of individual neurons down to subcellular resolution, which includes the propagation of axonal signals. However, axon reconstruction, to date, mainly relies on manual approaches to select the electrodes and channels that seemingly record the signals along a specific axon, while an automated approach to track multiple axonal branches in extracellular action-potential recordings is still missing. Approach: In this article, we propose a fully automated approach to reconstruct axons from extracellular electrical-potential landscapes, so-called 'electrical footprints' of neurons. After an initial electrode and channel selection, the proposed method first constructs a graph based on the voltage signal amplitudes and latencies. Then, the graph is interrogated to extract possible axonal branches. Finally, the axonal branches are pruned, and axonal action-potential propagation velocities are computed. Main results: We first validate our method using simulated data from detailed reconstructions of neurons, showing that our approach is capable of accurately reconstructing axonal branches. We then apply the reconstruction algorithm to experimental recordings of HD-MEAs and show that it can be used to determine axonal morphologies and signal-propagation velocities at high throughput. Significance: We introduce a fully automated method to reconstruct axonal branches and estimate axonal action-potential propagation velocities using HD-MEA recordings. Our method yields highly reliable and reproducible velocity estimations, which constitute an important electrophysiological feature of neuronal preparations.

Objective. Glioma growth may cause pervasive disruptions of brain vascular structure and function. Revealing both structural and functional alterations at a fine spatial scale is challenging for existing imaging techniques, which could confound the understanding of the basic mechanisms of brain diseases. Approach. In this study, we apply photoacoustic microscopy with a high spatial-temporal resolution and a wide field of view to investigate the glioma-induced alterations of cortical vascular morphology, hemodynamic response, as well as functional connectivity at resting- and stimulated- states. Main results. We find that glioma promotes the growth of microvessels and leads to the increase of vascular proportion in the cerebral cortex by deriving structural parameters. The glioma also causes the loss of response in the ipsilateral hemisphere and abnormal response in the contralateral hemisphere, and further induces brain-wide alterations of functional connectivity in resting and stimulated states. Significance. The observed results show the foundation of employing photoacoustic microscopy as a potential technique in revealing the underlying mechanisms of brain diseases.

Objective. A rapid serial visual presentation (RSVP)-based brain-computer interface (BCI) is an efficient information detection technology through detecting event-related potentials (ERPs) evoked by target visual stimuli. The BCI system requires a time-consuming calibration process to build a reliable decoding model for a new user. Therefore, zero-calibration has become an important topic in BCI research. Approach. In this paper, we construct an RSVP dataset that includes 31 subjects, and propose a zero-calibration method based on a metric-based meta-learning: ERP prototypical matching net (EPMN). EPMN learns a metric space where the distance between electroencephalography (EEG) features and ERP prototypes belonging to the same category is smaller than that of different categories. Here, we employ prototype learning to learn a common representation from ERP templates of different subjects as ERP prototypes. Additionally, a metric-learning loss function is proposed for maximizing the distance between different classes of EEG and ERP prototypes and minimizing the distance between the same classes of EEG and ERP prototypes in the metric space. Main results. The experimental results showed that EPMN achieved a balanced-accuracy of 86.34% and outperformed the comparable methods. Significance. Our EPMN can realize zero-calibration for an RSVP-based BCI system.

Objective. Epilepsy is a common neurological disorder in which patients suffer from sudden and unpredictable seizures. Seizures are caused by excessive and abnormal neuronal activity. Different methods have been employed to investigate electroencephalogram (EEG) data in patients with epilepsy. This paper introduces a simple yet accurate array-based method to study and predict seizures. Approach. We use the CHB-MIT dataset (all 24 cases), which includes scalp EEG recordings. The proposed method is based on the random matrix theory. After applying wavelet decomposition to denoise the data, we analyze the spatial coherence of the epileptic recordings by looking at the width of the covariance matrix eigenvalue distribution at different time and frequency bins. Main results. We train patient-specific support vector machine classifiers to distinguish between interictal and preictal data with high performance and a false prediction rate as low as 0.09 h⁻¹. The proposed technique achieves an average accuracy, specificity, sensitivity, and area under the curve of 99.05%, 93.56%, 99.09%, and 0.99, respectively. Significance. Our proposed method outperforms state-of-the-art works in terms of sensitivity while maintaining a low false prediction rate. Also, in contrast to neural networks, which may achieve high performance, this work provides high sensitivity without compromising interpretability.

Objective. Motor unit (MU) discharge information obtained via the online electromyogram (EMG) decomposition has shown promising prospects in multiple applications. However, the nonstationarity of EMG signals caused by the rotation (recruitment-derecruitment) of MUs and the variation of MU action potentials (MUAP) can significantly degrade the online decomposition performance. This study aimed to develop an independent component analysis-based online decomposition method that can accommodate the nonstationarity of EMG signals. Approach. The EMG nonstationarity can make the separation vectors obtained beforehand inaccurate, resulting in the reduced amplitudes of the peaks corresponding to firing events in the source signal (independent component) and then the decreased accuracy of firing events. Therefore, we utilized the FitzHugh–Nagumo (FHN) resonance model to enhance the firing peaks in the source signal in order to improve the decomposition accuracy. A two-session approach was used with the offline session to extract the separation vectors and train the FHN models. In the online session, the source signal was estimated and further processed using the FHN model before detecting the firing events in a real-time manner. The proposed method was tested on simulated EMG signals, in which MU rotation and MUAP variation were involved to mimic the nonstationarity of EMG recordings. Main results. Compared with the conventional method, the proposed method can improve the decomposition accuracy significantly (88.70% ± 4.17% vs. 92.43% ± 2.79%) by enhancing the firing peaks, and more importantly, the improvement was more prominent when the EMG signal had stronger background noises (87.00% ± 3.70% vs. 91.66% ± 2.63%). Conclusions. Our results demonstrated the effectiveness of the proposed method to utilize the FHN model to improve the online decomposition performance on the nonstationary EMG signals. Further development of our method has the potential to improve the performance of the neural decoding system that utilizes the MU discharge information and promote its application in the neural-machine interface.

Objective. Neural interfaces need to become more unobtrusive and socially acceptable to appeal to general consumers outside rehabilitation settings. Approach. We developed a non-invasive neural interface that provides access to spinal motor neuron activities from the wrist, which is the preferred location for a wearable. The interface decodes far-field potentials present at the tendon endings of the forearm muscles using blind source separation. First, we evaluated the reliability of the interface to detect motor neuron firings based on far-field potentials, and thereafter we used the decoded motor neuron activity for the prediction of finger contractions in offline and real-time conditions. Main results. The results showed that motor neuron activity decoded from the far-field potentials at the wrist accurately predicted individual and combined finger commands and therefore allowed for highly accurate real-time task classification. Significance. These findings demonstrate the feasibility of a non-invasive, neural interface at the wrist for precise real-time control based on the output of the spinal cord.

Objective. A fundamental challenge in optogenetics is to elicit long-term high-fidelity neuronal spiking with negligible heating. Fast channelrhodopsins (ChRs) require higher irradiances and cause spike failure due to photocurrent desensitization under sustained illumination, whereas, more light-sensitive step-function opsins (SFOs) exhibit prolonged depolarization with insufficient photocurrent and fast response for high-fidelity spiking. Approach. We present a novel method to overcome this fundamental limitation by co-expressing fast ChRs with SFOs. A detailed theoretical analysis of ChETA co-expressed with different SFOs, namely ChR2(C128A), ChR2(C128S), stabilized step-function opsin (SSFO) and step-function opsin with ultra-high light sensitivity (SOUL), expressing hippocampal neurons has been carried out by formulating their accurate theoretical models. Main results. ChETA-SFO-expressing hippocampal neurons shows more stable photocurrent that overcomes spike failure. Spiking fidelity in these neurons can be sustained even at lower irradiances of subsequent pulses (77% of initial pulse intensity in ChETA-ChR2(C128A)-expressing neurons) or by using red-shifted light pulses at appropriate intervals. High-fidelity spiking upto 60 Hz can be evoked in ChETA-ChR2(C128S), ChETA-SSFO and ChETA-SOUL-expressing neurons, which cannot be attained with only SFOs. Significance. The present study provides important insights about photostimulation protocols for bi-stable switching of neurons. This new approach provides a means for sustained low-power, high-frequency and high-fidelity optogenetic switching of neurons, necessary to study various neural functions and neurodegenerative disorders, and enhance the utility of optogenetics for biomedical applications.

Objective. Trauma induced by the insertion of microelectrodes into cortical neural tissue is a significant problem. Further, micromotion and mechanical mismatch between microelectrode probes and neural tissue is implicated in an adverse foreign body response (FBR). Hence, intracortical ultra-microelectrode probes have been proposed as alternatives that minimize this FBR. However, significant challenges in implanting these flexible probes remain. We investigated the insertion mechanics of amorphous silicon carbide (a-SiC) probes with a view to defining probe geometries that can be inserted into cortex without buckling. Approach. We determined the critical buckling force of a-SiC probes as a function of probe geometry and then characterized the buckling behavior of these probes by measuring force–displacement responses during insertion into agarose gel and rat cortex. Main results. Insertion forces for a range of probe geometries were determined and compared with critical buckling forces to establish geometries that should avoid buckling during implantation into brain. The studies show that slower insertion speeds reduce the maximum insertion force for single-shank probes but increase cortical dimpling during insertion of multi-shank probes. Significance. Our results provide a guide for selecting probe geometries and insertion speeds that allow unaided implantation of probes into rat cortex. The design approach is applicable to other animal models where insertion of intracortical probes to a depth of 2 mm is required.

Objective. Acute blockade of glutamate N-methyl-D-aspartate receptors by ketamine induces symptoms and electrophysiological changes similar to schizophrenia. Previous studies have shown that ketamine elicits aberrant gamma oscillations in several cortical areas and impairs coupling strength between the low-frequency phase and fast frequency amplitude, which plays an important role in integrating functional information. Approach. This study utilized a customized wireless electrocorticography (ECoG) recording device to collect subdural signals from the somatosensory and primary auditory cortices in two monkeys. Ketamine was administered at a dose of 3 mg kg⁻¹ (intramuscular) or 0.56 mg kg⁻¹ (intravenous) to elicit brain oscillation reactions. We analyzed the raw data using methods such as power spectral density, time-frequency spectra, and phase-amplitude coupling (PAC). Main results. Acute ketamine triggered broadband gamma and high gamma oscillation power and decreased lower frequencies. The effect was stronger in the primary auditory cortex than in the somatosensory area. The coupling strength between the low phase of theta and the faster amplitude of gamma/high gamma bands was increased by a lower dose (0.56 mg kg⁻¹ iv) and decreased with a higher dose (3 mg kg⁻¹ im) ketamine. Significance. Our results showed that lower and higher doses of ketamine elicited differential effects on theta-gamma PAC. These findings support the utility of ECoG models as a translational platform for pharmacodynamic research in future research.

Objective. Transcranial magnetic stimulation (TMS) is a clinically effective therapeutic instrument used to modulate neural activity. Despite three decades of research, two challenging issues remain, the possibility of changing the (a) stimulated spot and (b) stimulation type (real or sham) without physically moving the coil. In this study, a second-generation programmable TMS device with advanced stimulus shaping is introduced that uses a five-level cascaded H-bridge inverter and phase-shifted pulse-width modulation. The principal idea of this research is to obtain real, sham, and multi-locus stimulation using the same TMS system. Approach. We propose a two-channel modulation-based magnetic pulse generator and a novel coil arrangement, consisting of two circular coils with a physical distance of 20 mm between the coils and a control method for modifying the effective stimulus intensity, which leads to the live steerability of the target and type of stimulation. Main results. Based on the measured system performance, the stimulation profile can be steered ±20 mm along a line from the centroid of the coil locations by modifying the modulation index. Significance. The proposed system supports electronic control of the stimulation spot without physical coil movement, resulting in tunable modulation of targets, which is a crucial step towards automated TMS machines.

Objective. A P300-brain computer interface (P300-BCI) conveys a subject's intention through recognition of their event-related potentials (ERPs). However, in the case of visual stimuli, its performance depends strongly on eye gaze. When eye movement is impaired, it becomes difficult to focus attention on a target stimulus, and the quality of the ERP declines greatly, thereby affecting recognition efficiency. Approach. In this paper, the expectancy wave (E-wave) is proposed to improve signal quality and thereby improve identification of visual targets under the covert attention. The stimuli of the P300-BCI described here are presented in a fixed sequence, so the subjects can predict the next target stimulus and establish a stable expectancy effect of the target stimulus through training. Features from the E-wave that occurred 0 ∼ 300 ms before a stimulus were added to the post-stimulus ERP components for intention recognition. Main results. Comparisons of ten healthy subjects before and after training demonstrated that the expectancy wave generated before target stimulus could be used with the P300 component to improve character recognition accuracy (CRA) from 85% to 92.4%. In addition, CRA using only the expectancy component can reach 68.2%, which is significantly greater than random probability (16.7%). The results of this study indicate that the expectancy wave can be used to improve recognition efficiency for a gaze-independent P300-BCI, and that training contributes to induction and recognition of the potential. Significance. This study proposes an effective approach to an efficient gaze-independent P300-BCI system.

Objectives. Electroencephalography (EEG) can be used to decode selective attention in cochlear implant (CI) users. This work investigates if selective attention to an attended speech source in the presence of a concurrent speech source can predict speech understanding in CI users. Approach. CI users were instructed to attend to one out of two speech streams while EEG was recorded. Both speech streams were presented to the same ear and at different signal to interference ratios (SIRs). Speech envelope reconstruction of the to-be-attended speech from EEG was obtained by training decoders using regularized least squares. The correlation coefficient between the reconstructed and the attended (${\rho _{{{\text{A}}_{{\text{SIR}}}}}}{\text{)}}$ or the unattended $\left( {{\rho _{{{\text{U}}_{{\text{SIR}}}}}}} \right){\text{ }}$ speech stream at each SIR was computed. Additionally, we computed the difference correlation coefficient at the same $({\rho _{{\text{Diff}}}} = {\text{ }}{\rho _{{{\text{A}}_{{\text{SIR}}}}}} - {\rho _{{{\text{U}}_{{\text{SIR}}}}}})$ and opposite SIR (${\rho _{{\text{DiffOpp}}}} = {\text{ }}{\rho _{{{\text{A}}_{{\text{SIR}}}}}} - {\rho _{{{\text{U}}_{ - {\text{SIR}}}}}})$. ${\rho _{{\text{Diff}}}}$ compares the attended and unattended correlation coefficient to speech sources presented at different presentation levels depending on SIR. In contrast, ${\rho _{{\text{DiffOpp}}}}$ compares the attended and unattended correlation coefficients to speech sources presented at the same presentation level irrespective of SIR. Main results. Selective attention decoding in CI users is possible even if both speech streams are presented monaurally. A significant effect of SIR on ${\rho _{{{\text{A}}_{{\text{SIR}}}}}}$, ${\rho _{{\text{Diff}}}}$ and ρ_DiffOpp, but not on ${\rho _{{{\text{U}}_{{\text{SIR}}}}}}$, was observed. Finally, the results show a significant correlation between speech understanding performance and ${\rho _{{{\text{A}}_{{\text{SIR}}}}}}$ as well as with ${\rho _{{{\text{U}}_{{\text{SIR}}}}}}$ across subjects. Moreover, ρ_DiffOpp which is less affected by the CI artifact, also demonstrated a significant correlation with speech understanding. Significance. Selective attention decoding in CI users is possible, however care needs to be taken with the CI artifact and the speech material used to train the decoders. These results are important for future development of objective speech understanding measures for CI users.

Objective. Transcutaneous vagus nerve stimulation (tVNS) is a form of non-invasive brain stimulation that delivers a sequence of electrical pulses to the auricular branch of the vagus nerve and is used increasingly in the treatment of a number of health conditions such as epilepsy and depression. Recent research has focused on the efficacy of tVNS to treat different medical conditions, but there is little conclusive evidence concerning the optimal stimulation parameters. There are relatively few studies that have combined tVNS with a neuroimaging modality, and none that have attempted simultaneous magnetoencephalography (MEG) and tVNS due to the presence of large stimulation artifacts produced by the electrical stimulation which are many orders of magnitude larger than underlying brain activity. Approach. The aim of this study is to investigate the utility of MEG to gain insight into the regions of the brain most strongly influenced by tVNS and how variation of the stimulation parameters can affect this response in healthy participants. Main results. We have successfully demonstrated that MEG can be used to measure brain response to tVNS. We have also shown that varying the stimulation frequency can lead to a difference in brain response, with the brain also responding in different anatomical regions depending on the frequency. Significance. The main contribution of this paper is to demonstrate the feasibility of simultaneous pulsed tVNS and MEG recording, allowing direct investigation of the changes in brain activity that result from different stimulation parameters. This may lead to the development of customised therapeutic approaches for the targeted treatment of different conditions.

Objective. To take full advantage of both labeled data and unlabeled ones, the Graph Convolutional Network (GCN) was introduced in electroencephalography (EEG) based emotion recognition to achieve feature propagation. However, a single feature cannot represent the emotional state entirely and precisely due to the instability of the EEG signal and the complexity of the emotional state. In addition, the noise existing in the graph may affect the performance greatly. To solve these problems, it was necessary to introduce feature/similarity fusion and noise reduction strategies. Approach. A semi-supervised EEG emotion recognition model combining graph fusion, network enhancement, and feature fusion was proposed. Firstly, different features were extracted from EEG and then compacted by Principal Component Analysis (PCA), respectively. Secondly, a Sample-by-sample Similarity Matrix (SSM) was constructed based on each feature, and similarity network fusion (SNF) was adopted to fuse the graphs corresponding to different SSMs to take advantage of their complementarity. Then, Network Enhancement (NE) was performed on the fused graph to reduce the noise in it. Finally, GCN was performed on the concatenated features and the enhanced fused graph to achieve feature propagation. Main results. Experimental results demonstrated that: (a) When 5.30$\%$ of SEED and 7.20$\%$ of SEED-IV samples were chosen as the labeled samples, respectively, the minimum classification accuracy improvement achieved by the proposed scheme over state-of-the-art schemes were 1.52$\%$ on SEED and 13.14$\%$ on SEED-IV, respectively. (b) When 8.00$\%$ of SEED and 9.60$\%$ of SEED-IV samples were chosen as the labeled samples, respectively, the minimum training time reduction achieved by the proposed scheme over state-of-the-art schemes were 46.75 s and 22.55 s, respectively. (c) Graph fusion, network enhancement, and feature fusion all contributed to the performance enhancement. (d) The key hyperparameters that affect the performance were relatively few and easy to set to obtain outstanding performance. Significance. This paper demonstrated that the combination of graph fusion, network enhancement, and feature fusion help to enhance GCN-based EEG emotion recognition.

Objective. While decoders of electroencephalography-based event-related potentials (ERPs) are routinely tailored to the individual user to maximize performance, developing them on populations for individual usage has proven much more challenging. We propose the analytic beamformer transformation (ABT) to extract phase and/or magnitude information from spatiotemporal ERPs in response to motion-onset stimulation. Approach. We have tested ABT on 52 motion-onset visual evoked potential (mVEP) datasets from 26 healthy subjects and compared the classification accuracy of support vector machine (SVM), spatiotemporal beamformer (stBF) and stepwise linear discriminant analysis (SWLDA) when trained on individual subjects and on a population thereof. Main results. When using phase- and combined phase/magnitude information extracted by ABT, we show significant improvements in accuracy of population-trained classifiers applied to individual users (p < 0.001). We also show that 450 epochs are needed for a correct functioning of ABT, which corresponds to 2 min of paradigm stimulation. Significance. We have shown that ABT can be used to create population-trained mVEP classifiers using a limited number of epochs. We expect this to pertain to other ERPs or synchronous stimulation paradigms, allowing for a more effective, population-based training of visual BCIs. Finally, as ABT renders recordings across subjects more structurally invariant, it could be used for transfer learning purposes in view of plug-and-play BCI applications.

Objective. Evoked tactile sensation (ETS) elicited by transcutaneous electrical nerve stimulation (TENS) is promising to convey digit-specific sensory information to amputees naturally and non-invasively. Fitting ETS-based sensory feedback to amputees entails customizing coding of multiple sensory information for each stimulation site. This study was to elucidate the consistency of percepts and qualities by TENS at multiple stimulation sites in amputees retaining ETS. Approach. Five transradial amputees with ETS and fourteen able-bodied subjects participated in this study. Surface electrodes with small size (10 mm in diameter) were adopted to fit the restricted projected finger map on the forearm stump of amputees. Effects of stimulus frequency on sensory types were assessed, and the map of perceptual threshold for each sensation was characterized. Sensitivity for vibration and buzz sensations was measured using distinguishable difference in stimulus pulse width. Rapid assessments for modulation ranges of pulse width at fixed amplitude and frequency were developed for coding sensory information. Buzz sensation was demonstrated for location discrimination relating to prosthetic fingers. Main results. Vibration and buzz sensations were consistently evoked at 20 Hz and 50 Hz as dominant sensation types in all amputees and able-bodied subjects. Perceptual thresholds of different sensations followed a similar strength-duration curve relating stimulus amplitude to pulse width. The averaged distinguishable difference in pulse width was 12.84 ± 7.23 μs for vibration and 15.21 ± 6.47 μs for buzz in able-bodied subjects, and 14.91 ± 10.54 μs for vibration and 11.30 ± 3.42 μs for buzz in amputees. Buzz coding strategy enabled five amputees to discriminate contact of individual fingers with an overall accuracy of 77.85%. Significance. The consistency in perceptual qualities of dominant sensations can be exploited for coding multi-modality sensory feedback. A fast protocol of sensory coding is possible for fitting ETS-based, non-invasive sensory feedback to amputees.

Objective. Ear-EEG (electroencephalography) allows to record brain activity using only a few electrodes located close to the ear. Ear-EEG is comfortable and easy to apply, facilitating beyond-the-lab EEG recordings in everyday life. With the unobtrusive setup, a person wearing it can blend in, allowing unhindered EEG recordings in social situations. However, compared to classical cap-EEG, only a small part of the head is covered with electrodes. Most scalp positions that are known from established EEG research are not covered by ear-EEG electrodes, making the comparison between the two approaches difficult and might hinder the transition from cap-based lab studies to ear-based beyond-the-lab studies. Approach. We here provide a reference data-set comparing ear-EEG and cap-EEG directly for four different auditory event-related potentials (ERPs): N100, MMN, P300 and N400. We show how the ERPs are reflected when using only electrodes around the ears. Main results. We find that significant condition differences for all ERP-components could be recorded using only ear-electrodes. The effect sizes were moderate to high on the single subject level. Morphology and temporal evolution of signals recorded from around-the-ear resemble highly those from standard scalp-EEG positions. We found a reduction in effect size (signal loss) for the ear-EEG electrodes compared to cap-EEG of 21%–44%. The amount of signal loss depended on the ERP-component; we observed the lowest percentage signal loss for the N400 and the highest percentage signal loss for the N100. Our analysis further shows that no single channel position around the ear is optimal for recording all ERP-components or all participants, speaking in favor of multi-channel ear-EEG solutions. Significance. Our study provides reference results for future studies employing ear-EEG.

Objective. Revealing the relationship between simultaneous scalp electroencephalography (EEG) and intracranial electroencephalography (iEEG) is of great importance for both neuroscientific research and translational applications. However, whether prominent iEEG features in the high-gamma band can be reflected by scalp EEG is largely unknown. To address this, we investigated the phase-amplitude coupling (PAC) phenomenon between the low-frequency band of scalp EEG and the high-gamma band of iEEG. Approach. We analyzed a simultaneous iEEG and scalp EEG dataset acquired under a verbal working memory paradigm from nine epilepsy subjects. The PAC values between pairs of scalp EEG channel and identified iEEG channel were explored. After identifying the frequency combinations and electrode locations that generated the most significant PAC values, we compared the PAC values of different task periods (encoding, maintenance, and retrieval) and memory loads. Main results. We demonstrated that the amplitude of high-gamma activities in the entorhinal cortex, hippocampus, and amygdala was correlated to the delta or theta phase at scalp locations such as Cz and Pz. In particular, the frequency bin that generated the maximum PAC value centered at 3.16–3.84 Hz for the phase and 50–85 Hz for the amplitude. Moreover, our results showed that PAC values for the retrieval period were significantly higher than those of the encoding and maintenance periods, and the PAC was also influenced by the memory load. Significance. This is the first human simultaneous iEEG and scalp EEG study demonstrating that the amplitude of iEEG high-gamma components is associated with the phase of low-frequency components in scalp EEG. These findings enhance our understanding of multiscale neural interactions during working memory, and meanwhile, provide a new perspective to estimate intracranial high-frequency features with non-invasive neural recordings.

Objective. Persons with tetraplegia can use brain-machine interfaces to make visually guided reaches with robotic arms. Without somatosensory feedback, these movements will likely be slow and imprecise, like those of persons who retain movement but have lost proprioception. Intracortical microstimulation (ICMS) has promise for providing artificial somatosensory feedback. ICMS that mimics naturally occurring neural activity, may allow afferent interfaces that are more informative and easier to learn than stimulation evoking unnaturalistic activity. To develop such biomimetic stimulation patterns, it is important to characterize the responses of neurons to ICMS. Approach. Using a Utah multi-electrode array, we recorded activity evoked by both single pulses and trains of ICMS at a wide range of amplitudes and frequencies in two rhesus macaques. As the electrical artifact caused by ICMS typically prevents recording for many milliseconds, we deployed a custom rapid-recovery amplifier with nonlinear gain to limit signal saturation on the stimulated electrode. Across all electrodes after stimulation, we removed the remaining slow return to baseline with acausal high-pass filtering of time-reversed recordings. Main results. After single pulses of stimulation, we recorded what was likely transsynaptically-evoked activity even on the stimulated electrode as early as ∼0.7 ms. This was immediately followed by suppressed neural activity lasting 10–150 ms. After trains, this long-lasting inhibition was replaced by increased firing rates for ∼100 ms. During long trains, the evoked response on the stimulated electrode decayed rapidly while the response was maintained on non-stimulated channels. Significance. The detailed description of the spatial and temporal response to ICMS can be used to better interpret results from experiments that probe circuit connectivity or function of cortical areas. These results can also contribute to the design of stimulation patterns to improve afferent interfaces for artificial sensory feedback.

Objective. Determining the roles of underlying mechanisms involved in stabilizing the human trunk during sitting is a fundamental challenge in human motor control. However, distinguishing their roles requires understanding their complex interrelations and describing them with physiologically meaningful neuromechanical parameters. The literature has shown that such mechanistic understanding contributes to diagnosing and improving impaired balance as well as developing assistive technologies for restoring trunk stability. This study aimed to provide a comprehensive characterization of the underlying neuromuscular stabilization mechanisms involved in human sitting. Approach. This study characterized passive and active stabilization mechanisms involved in seated stability by identifying a nonlinear neuromechanical physiologically-meaningful model in ten able-bodied individuals during perturbed sitting via an adaptive unscented Kalman filter to account for the nonlinear time-varying process and measurement noises. Main results. We observed that the passive mechanism provided instant resistance against gravitational disturbances, whereas the active mechanism provided delayed complementary phasic response against external disturbances by activating appropriate trunk muscles while showing non-isometric behavior. The model predicted the trunk sway behavior during perturbed sitting with high accuracy and correlation (average: 0.0007 (rad²) and 86.77%). This allows a better mechanistic understanding of the roles of passive and active stabilization mechanisms involved in sitting. Significance. Our characterization approach accounts for the inherently nonlinear behavior of the neuromuscular mechanisms and physiological uncertainties, while allowing for real-time tracking and correction of parameters' variations due to external disturbances and muscle fatigue. The outcome of our research, for the first time, (a) allows a better mechanistic understanding of the roles of passive and active stabilization mechanisms involved in sitting; (b) enables objective evaluation and targeted rehabilitative interventions for impaired balance; facilitate bio-inspired designs of assistive technologies, and (c) opens new horizons in mathematical identification of neuromechanical mechanisms employed in the stable control of human body postures and motions.

Objective. Iron core coils offer a passive way to increase the induced electric field intensity during transcranial magnetic stimulation (TMS), but the influences of core position and dimensions on coil performance have not been elaborately discussed before. Approach. In this study, with the basic figure-of-eight (Fo8) and slinky coil structures, iron core coil optimization is performed with the finite element method considering core position and dimensions. A performance factor combining performance parameters, including the maximum induced electric field, stimulation depth, focus, and heat loss, is utilized to evaluate the comprehensive coil performance. Main results. According to the performance factor, both iron core coils obtain the best overall performance with a fill factor 0.4 and the two legs of the iron core close to the inner sides of the coil. Finally, three prototypes are constructed—the basic, optimized, and full-size slinky iron core coil—and magnetic field detection demonstrates a good agreement with the simulation results. Significance. The proposed systematic optimization approach for iron core coil based on Fo8 and slinky basic structure can be applied to improve TMS coil performance, reduce power requirements, and guide the design of other iron core TMS coils.

Objective. Brain–computer interfaces (BCIs) have the potential to bypass damaged neural pathways and restore functionality lost due to injury or disease. Approaches to decoding kinematic information are well documented; however, the decoding of kinetic information has received less attention. Additionally, the possibility of using stereo-electroencephalography (SEEG) for kinetic decoding during hand grasping tasks is still largely unknown. Thus, the objective of this paper is to demonstrate kinetic parameter decoding using SEEG in patients performing a grasping task with two different force levels under two different ascending rates. Approach. Temporal-spectral representations were studied to investigate frequency modulation under different force tasks. Then, force amplitude was decoded from SEEG recordings using multiple decoders, including a linear model, a partial least squares model, an unscented Kalman filter, and three deep learning models (shallow convolutional neural network, deep convolutional neural network and the proposed CNN+RNN neural network). Main results. The current study showed that: (a) for some channel, both low-frequency modulation (event-related desynchronization (ERD)) and high-frequency modulation (event-related synchronization) were sustained during prolonged force holding periods; (b) continuously changing grasp force can be decoded from the SEEG signals; (c) the novel CNN+RNN deep learning model achieved the best decoding performance, with the predicted force magnitude closely aligned to the ground truth under different force amplitudes and changing rates. Significance. This work verified the possibility of decoding continuously changing grasp force using SEEG recordings. The result presented in this study demonstrated the potential of SEEG recordings for future BCI application.

Objective. Proprioception is the sense of one's position, orientation, and movement in space, and it is of fundamental importance for motor control. When proprioception is impaired or absent, motor execution becomes error-prone, leading to poorly coordinated movements. The kinaesthetic illusion, which creates perceptions of limb movement in humans through non-invasively applying vibrations to muscles or tendons, provides an avenue for studying and restoring the sense of joint movement (kinaesthesia). This technique, however, leaves ambiguity between proprioceptive percepts that arise from muscles versus those that arise from skin receptors. Here we propose the concept of a stimulation system to activate kinaesthesia through the untethered application of localized vibration through implanted magnets. Approach. In this proof-of-concept study, we use two simplified one-DoF systems to show the feasibility of eliciting muscle-sensory responses in an animal model across multiple frequencies, including those that activate the kinaesthetic illusion (70–115 Hz). Furthermore, we generalized the concept by developing a five-DoF prototype system capable of generating directional, frequency-selective vibrations with desired displacement profiles. Main results. In-vivo tests with the one-DoF systems demonstrated the feasibility to elicit muscle sensory neural responses in the median nerve of an animal model. Instead, in-vitro tests with the five-DoF prototype demonstrated high accuracy in producing directional and frequency selective vibrations along different magnet axes. Significance. These results provide evidence for a new technique that interacts with the native neuro-muscular anatomy to study proprioception and eventually pave the way towards the development of advanced limb prostheses or assistive devices for the sensory impaired.

Objective. Consistent transmission of data from wireless neural devices is critical for monitoring the condition and performance of stimulation electrodes. To date, no wireless neural interface has demonstrated the ability to monitor the integrity of chronically implanted electrodes through wireless data transmission. Approach. In this work, we present a method for wirelessly recording the voltage transient (VT) response to constant-current, cathodic-first asymmetric pulsing from a microelectrode array. We implanted six wireless devices in rat sciatic nerve and wirelessly recorded VT measurements throughout a 38 week implantation period. Main results. Electrode maximum cathodic potential excursion (E_mc), access voltage, and driving voltage (extracted from each VT) remained stable throughout the 38 week study period. Average E_mc (from an applied +0.6 V interpulse bias) in response to 4.7 µA, 200.2 µs pulses was 267 ± 107 mV at week 1 post-implantation and 282 ± 52 mV at week 38 post-implantation. Access voltage for the same 4.7 µA pulsing amplitude was 239 ± 65 mV at week 1 post-implantation and 268 ± 139 mV at week 38 post-implantation. Significance. The VT response recorded via reverse telemetry from the wireless microelectrode array did not significantly change over a 38 week implantation period and was similar to previously reported VTs from wired microelectrodes with the same geometry. Additionally, the VT response recorded wirelessly in phosphate buffered saline before and after device implantation appeared as expected, showing significantly less electrode polarization and smaller access voltage than the VT response in vivo.

Objective. Transcranial magnetic stimulation (TMS) can modulate brain function via an electric field (E-field) induced in a brain region of interest (ROI). The ROI E-field can be computationally maximized and set to match a specific reference using individualized head models to find the optimal coil placement and stimulus intensity. However, the available software lacks many practical features for prospective planning of TMS interventions and retrospective evaluation of the experimental targeting accuracy. Approach. The TMS targeting and analysis pipeline (TAP) software uses an MRI/fMRI-derived brain target to optimize coil placement considering experimental parameters such as the subject's hair thickness and coil placement restrictions. The coil placement optimization is implemented in SimNIBS 3.2, for which an additional graphical user interface (TargetingNavigator) is provided to visualize/adjust procedural parameters. The coil optimization process also computes the E-field at the target, allowing the selection of the TMS device intensity setting to achieve specific E-field strengths. The optimized coil placement information is prepared for neuronavigation software, which supports targeting during the TMS procedure. The neuronavigation system can record the coil placement during the experiment, and these data can be processed in TAP to quantify the accuracy of the experimental TMS coil placement and induced E-field. Main results. TAP was demonstrated in a study consisting of three repetitive TMS sessions in five subjects. TMS was delivered by an experienced operator under neuronavigation with the computationally optimized coil placement. Analysis of the experimental accuracy from the recorded neuronavigation data indicated coil location and orientation deviations up to about 2 mm and 2°, respectively, resulting in an 8% median decrease in the target E-field magnitude compared to the optimal placement. Significance. TAP supports navigated TMS with a variety of features for rigorous and reproducible stimulation delivery, including planning and evaluation of coil placement and intensity selection for E-field-based dosing.

The emotion recognition with electroencephalography (EEG) has been widely studied using the deep learning methods, but the topology of EEG channels is rarely exploited completely. In this paper, we propose a self-attention coherence clustering based on multi-pooling graph convolutional network (SCC-MPGCN) model for EEG emotion recognition. The adjacency matrix is constructed based on phase-locking value to describe the intrinsic relationship between different EEG electrodes as graph signals. The graph Laplacian matrix is obtained from the adjacency matrix and then is fed into the graph convolutional layers to learn the generalized features. Moreover, we propose a novel graph coarsening method called SCC, using the coherence to cluster the nodes. The benefits are that the dimensionality of adjacency matrix can be reduced and the global information can be achieved from the raw data. Meanwhile, a MPGCN block is introduced to learn the generalized features of emotional states. The fully-connected layer and a softmax layer are adopted to derive the final classification results. We carry out the extensive experiments on DEAP dataset and the results show that the proposed method has better classification results than the state-of-the-art methods with the ten-fold cross-validation. And the model achieves the emotion recognition performance with a mean accuracy of 96.37%, 97.02%, 96.72% on valence, arousal, and dominance dimension, respectively.

Objective. Electroencephalogram (EEG) recordings are often contaminated with artifacts. Various methods have been developed to eliminate or weaken the influence of artifacts. However, most of them rely on prior experience for analysis. Approach. Here, we propose an deep learning framework to separate neural signal and artifacts in the embedding space and reconstruct the denoised signal, which is called DeepSeparator. DeepSeparator employs an encoder to extract and amplify the features in the raw EEG, a module called decomposer to extract the trend, detect and suppress artifact and a decoder to reconstruct the denoised signal. Besides, DeepSeparator can extract the artifact, which largely increases the model interpretability. Main results. The proposed method is tested with a semi-synthetic EEG dataset and a real task-related EEG dataset, suggesting that DeepSeparator outperforms the conventional models in both EOG and EMG artifact removal. Significance. DeepSeparator can be extended to multi-channel EEG and data with any arbitrary length. It may motivate future developments and application of deep learning-based EEG denoising. The code for DeepSeparator is available at https://github.com/ncclabsustech/DeepSeparator.

Objective. Transcranial magnetic stimulation (TMS) is an experimental therapy for promoting motor recovery from hemiparesis. At present, hemiparesis patients' responses to TMS are variable. To maximize its therapeutic potential, we need an approach that relates the electrophysiology of motor recovery and TMS. To this end, we propose corticomuscular network (CMN) representing the holistic motor system, including the cortico-cortical pathway, corticospinal tract, and muscle co-activation. Approach. CMN is made up of coherence between pairs of electrode signals and spatial locations of the electrodes. We associated coherence and graph features of CMN with Fugl-Meyer Assessment (FMA) for the upper extremity. Besides, we compared CMN between 8 patients with hemiparesis and 6 healthy controls and contrasted CMN of patients before and after a 1 Hz TMS. Main results. Corticomuscular coherence (CMC) correlated positively with FMA. The regression model between FMA and CMC between five pairs of channels had 0.99 adjusted and a p-value less than 0.01. Compared to healthy controls, CMN of patients tended to be a small-world network and was more interconnected with higher CMC. CMC between cortex and triceps brachii long head was higher in patients. 15 min 1 Hz TMS protocol induced coherence changes beyond the stimulation side and had a limited impact on CMN parameters that are related to motor recovery. Significance. CMN is a potential clinical approach to quantify rehabilitating progress. It also sheds light on the desirable electrophysiological effects of TMS based on which rehabilitating strategies can be optimized.

Objective. Fast neural electrical impedance tomography is an imaging technique that has been successful in visualising electrically evoked activity of myelinated fibres in peripheral nerves by measurement of the impedance changes (dZ) accompanying excitation. However, imaging of unmyelinated fibres is challenging due to temporal dispersion (TP) which occurs due to variability in conduction velocities of the fibres and leads to a decrease of the signal below the noise with distance from the stimulus. To overcome TP and allow electrical impedance tomography imaging in unmyelinated nerves, a new experimental and signal processing paradigm is required allowing dZ measurement further from the site of stimulation than compound neural activity is visible. The development of such a paradigm was the main objective of this study. Approach. A finite element-based statistical model of TP in porcine subdiaphragmatic nerve was developed and experimentally validated ex-vivo. Two paradigms for nerve stimulation and processing of the resulting data—continuous stimulation and trains of stimuli, were implemented; the optimal paradigm for recording dispersed dZ in unmyelinated nerves was determined. Main results. While continuous stimulation and coherent spikes averaging led to higher signal-to-noise ratios (SNRs) at close distances from the stimulus, stimulation by trains was more consistent across distances and allowed dZ measurement at up to 15 cm from the stimulus (SNR = 1.8 ± 0.8) if averaged for 30 min. Significance. The study develops a method that for the first time allows measurement of dZ in unmyelinated nerves in simulation and experiment, at the distances where compound action potentials are fully dispersed.

Objective. The aim of this review was to systematically identify the ethical implications of visual neuroprostheses. Approach. A systematic search was performed in both PubMed and Embase using a search string that combined synonyms for visual neuroprostheses, brain–computer interfaces (BCIs), cochlear implants (CIs), and ethics. We chose to include literature on BCIs and CIs, because of their ethically relavant similarities and functional parallels with visual neuroprostheses. Main results. We included 84 articles in total. Six focused specifically on visual prostheses. The other articles focused more broadly on neurotechnologies, on BCIs or CIs. We identified 169 ethical implications that have been categorized under seven main themes: (a) benefits for health and well-being; (b) harm and risk; (c) autonomy; (d) societal effects; (e) clinical research; (f) regulation and governance; and (g) involvement of experts, patients and the public. Significance. The development and clinical use of visual neuroprostheses is accompanied by ethical issues that should be considered early in the technological development process. Though there is ample literature on the ethical implications of other types of neuroprostheses, such as motor neuroprostheses and CIs, there is a significant gap in the literature regarding the ethical implications of visual neuroprostheses. Our findings can serve as a starting point for further research and normative analysis.

Objective. Diagnosing epilepsy still requires visual interpretation of electroencephalography (EEG) and magnetoencephalography (MEG) by specialists, which prevents quantification and standardization of diagnosis. Previous studies proposed automated diagnosis by combining various features from EEG and MEG, such as relative power (Power) and functional connectivity (FC). However, the usefulness of interictal phase–amplitude coupling (PAC) in diagnosing epilepsy is still unknown. We hypothesized that resting-state PAC would be different for patients with epilepsy in the interictal state and for healthy participants such that it would improve discrimination between the groups. Approach. We obtained resting-state MEG and magnetic resonance imaging (MRI) in 90 patients with epilepsy during their preoperative evaluation and in 90 healthy participants. We used the cortical currents estimated from MEG and MRI to calculate Power in the δ (1–3 Hz), θ (4–7 Hz), α (8–13 Hz), β (13–30 Hz), low γ (35–55 Hz), and high γ (65–90 Hz) bands and FC in the θ band. PAC was evaluated using the synchronization index (SI) for eight frequency band pairs: the phases of δ, θ, α, and β and the amplitudes of low and high γ. First, we compared the mean SI values for the patients with epilepsy and the healthy participants. Then, using features such as PAC, Power, FC, and features extracted by deep learning (DL) individually or combined, we tested whether PAC improves discrimination accuracy for the two groups. Main results. The mean SI values were significantly different for the patients with epilepsy and the healthy participants. The SI value difference was highest for θ/low γ in the temporal lobe. Discrimination accuracy was the highest, at 90%, using the combination of PAC and DL. Significance. Abnormal PAC characterized the patients with epilepsy in the interictal state compared with the healthy participants, potentially improving the discrimination of epilepsy.

Objective. To perform automatic sleep scoring based only on intracranial electroencephalography (iEEG), without the need for scalp EEG), electrooculography (EOG) and electromyography (EMG), in order to study sleep, epilepsy, and their interaction. Approach. Data from 33 adult patients was used for development and training of the automatic scoring algorithm using both oscillatory and non-oscillatory spectral features. The first step consisted in unsupervised clustering of channels based on feature variability. For each cluster the classification was done in two steps, a multiclass tree followed by binary classification trees to distinguish the more challenging stage N1. The test data consisted in 11 patients, in whom the classification was done independently for each channel and then combined to get a single stage per epoch. Main results. An overall agreement of 78% was observed in the test set between the sleep scoring of the algorithm using iEEG alone and two human experts scoring based on scalp EEG, EOG and EMG. Balanced sensitivity and specificity were obtained for the different sleep stages. The performance was excellent for stages W, N2, and N3, and good for stage R, but with high variability across patients. The performance for the challenging stage N1 was poor, but at a similar level as for published algorithms based on scalp EEG. High confidence epochs in different stages (other than N1) can be identified with median per patient specificity >80%. Significance. The automatic algorithm can perform sleep scoring of long-term recordings of patients with intracranial electrodes undergoing presurgical evaluation in the absence of scalp EEG, EOG and EMG, which are normally required to define sleep stages but are difficult to use in the context of intracerebral studies. It also constitutes a valuable tool to generate hypotheses regarding local aspects of sleep, and will be significant for sleep evaluation in clinical epileptology and neuroscience research.

Objective. Mental workload is the result of the interactions between the demands of an operation task, the environment in which the task is performed, and the skills, behavior and perception of the performer. Working under a high mental workload can significantly affect an operator's ability to choose optimal decisions, judgments and motor actions while operating an unmanned aerial vehicle (UAV). However, the effect of mental schema, which reflects the level of expertise of an operator, on mental workload remains unclear. Here, we propose a theoretical framework for describing how the evolution of mental schema affects mental workload from the perspective of cognitive processing. Approach. We recruited 51 students to participate in a 10-day simulated quadrotor UAV flight training exercise. The EEG power spectral density (PSD)-based metrics were used to investigate the changes in neural responses caused by variations in the mental workload at different stages of mental schema evolution. Main results. It was found that the mental schema evolution influenced the direction and change trends of the frontal theta PSD, parietal alpha PSD, and central beta PSD, which are EEG indicators of mental workload. Initially, before the mental schema was formed, only the frontal theta PSD increased with increasing task difficulty; when the mental schema was initially being developed, the frontal theta PSD and the parietal alpha PSD decreased with increasing task difficulty, while the central beta PSD increased with increasing task difficulty. Finally, as the mental schema gradually matured, the trend of the three indicators did not change with increasing task difficulty. However, differences in the frontal PSD became more pronounced across task difficulty levels, while differences in the parietal PSD narrowed. Significance. Our results describe the relationship between the EEG PSD and the mental workload of UAV operators as the mental schema evolved. This suggests that EEG activity can be used to identify the mental schema and mental workload experienced by operators while performing a task, which can not only provide more accurate measurements of mental workload but also provide insights into the development of an operator's skill level.

Objective. A novel angle-tuned ring coil is proposed for improving the depth-spread performance of transcranial magnetic stimulation (TMS) coils and serve as the building blocks for high-performance composite coils and multisite TMS systems. Approach. Improving depth-spread performance by reducing field divergence through creating a more elliptical emitted field distribution from the coil. To accomplish that, instead of enriching the Fourier components along the planarized (x-y) directions, which requires different arrays to occupy large brain surface areas, we worked along the radial (z) direction by using tilted coil angles and stacking coil numbers to reduce the divergence of the emitted near field without occupying large head surface areas. The emitted electric field distributions were theoretically simulated in spherical and real human head models to analyze the depth-spread performance of proposed coils and compare with existing figure-8 coils. The results were then experimentally validated with field probes and in-vivo animal tests. Main results. The proposed 'angle-tuning' concept improves the depth-spread performance of individual coils with a significantly smaller footprint than existing and proposed coils. For composite structures, using the proposed coils as basic building blocks simplifies the design and manufacturing process and helps accomplish a leading depth-spread performance. In addition, the footprint of the proposed system is intrinsically small, making them suitable for multisite stimulations of inter and intra-hemispheric brain regions with an improved spread and less electric field divergence. Significance. Few brain functions are operated by isolated single brain regions but rather by coordinated networks involving multiple brain regions. Simultaneous or sequential multisite stimulations may provide tools for mechanistic studies of brain functions and the treatment of neuropsychiatric disorders. The proposed AT coil goes beyond the traditional depth-spread tradeoff rule of TMS coils, which provides the possibility of building new composite structures and new multisite TMS tools.

Objective. The application of cerebellar transcranial alternating current stimulation (tACS) is limited by the absence of commonly agreed montages and also the presence of unpleasant side effects. We aimed to find the most effective cerebellar tACS montage with minimum side effects (skin sensations and phosphenes). Approach. We first simulated cerebellar tACS with five montages (return electrode on forehead, buccinator, jaw, and neck positions, additionally focal montage with high-definition ring electrodes) to compare induced cerebellar current, then stimulated healthy participants and evaluated side effects for different montages and varying stimulation frequencies. Main results. The simulation revealed a descending order of current density in the cerebellum from forehead to buccinator, jaw, neck and ring montage respectively. Montages inducing higher current intensity in the eyeballs during the simulation resulted in stronger and broader phosphenes during tACS sessions. Strong co-stimulation of the brainstem was observed for the neck. Skin sensations did not differ between montages or frequencies. We propose the jaw montage as an optimal choice for maximizing cerebellar stimulation while minimizing unwanted side effects. Significance. These findings contribute to adopting a standard cerebellar tACS protocol. The combination of computational modelling and experimental data offers improved experimental control, safety, effectiveness, and reproducibility to all brain stimulation practices.