Table of contents

Objective. The advancement of surface electromyogram (sEMG) recording and signal processing techniques has allowed us to characterize the recruitment properties of a substantial population of motor units (MUs) non-invasively. Here we seek to determine whether MU recruitment properties are modified in paretic muscles of hemispheric stroke survivors. Approach. Using an advanced EMG sensor array, we recorded sEMG during isometric contractions of the first dorsal interosseous muscle over a range of contraction levels, from 20% to 60% of maximum, in both paretic and contralateral muscles of stroke survivors. Using MU decomposition techniques, MU action potential amplitudes and recruitment thresholds were derived for simultaneously activated MUs in each isometric contraction. Main results. Our results show a significant disruption of recruitment organization in paretic muscles, in that the size principle describing recruitment rank order was materially distorted. MUs were recruited over a very narrow force range with increasing force output, generating a strong clustering effect, when referenced to recruitment force magnitude. Such disturbances in MU properties also correlated well with the impairment of voluntary force generation. Significance. Our findings provide direct evidence regarding MU recruitment modifications in paretic muscles of stroke survivors, and suggest that these modifications may contribute to weakness for voluntary contractions.

Objective. The goal of this study is to characterize the phenomenon of evoked tactile sensation (ETS) on the stump skin of forearm amputees using transcutaneous electrical nerve stimulation (TENS). Approach. We identified the projected finger map (PFM) of ETS on the stump skin in 11 forearm amputees, and compared perceptual attributes of the ETS in nine forearm amputees and eight able-bodied subjects using TENS. The profile of perceptual thresholds at the most sensitive points (MSPs) in each finger-projected area was obtained by modulating current amplitude, pulse width, and frequency of the biphasic, rectangular current stimulus. The long-term stability of the PFM and the perceptual threshold of the ETS were monitored in five forearm amputees for a period of 11 months. Main results. Five finger-specific projection areas can be independently identified on the stump skin of forearm amputees with a relatively long residual stump length. The shape of the PFM was progressively similar to that of the hand with more distal amputation. Similar sensory modalities of touch, pressure, buzz, vibration, and numb below pain sensation could be evoked both in the PFM of the stump skin of amputees and in the normal skin of able-bodied subjects. Sensory thresholds in the normal skin of able-bodied subjects were generally lower than those in the stump skin of forearm amputees, however, both were linearly modulated by current amplitude and pulse width. The variation of the MSPs in the PFM was confined to a small elliptical area with 95% confidence. The perceptual thresholds of thumb-projected areas were found to vary less than 0.99 × 10⁻² mA cm⁻². Significance. The stable PFM and sensory thresholds of ETS are desirable for a non-invasive neural interface that can feed back finger-specific tactile information from the prosthetic hand to forearm amputees.

Objective. Current approaches in neurofeedback/brain–computer interface research often focus on identifying, on a subject-by-subject basis, the neural regions that are best suited for self-driven modulation. It is known that the hMT+/V5 complex, an early visual cortical region, is recruited during explicit and implicit motion imagery, in addition to real motion perception. This study tests the feasibility of training healthy volunteers to regulate the level of activation in their hMT+/V5 complex using real-time fMRI neurofeedback and visual motion imagery strategies. Approach. We functionally localized the hMT+/V5 complex to further use as a target region for neurofeedback. An uniform strategy based on motion imagery was used to guide subjects to neuromodulate hMT+/V5. Main results. We found that 15/20 participants achieved successful neurofeedback. This modulation led to the recruitment of a specific network as further assessed by psychophysiological interaction analysis. This specific circuit, including hMT+/V5, putative V6 and medial cerebellum was activated for successful neurofeedback runs. The putamen and anterior insula were recruited for both successful and non-successful runs. Significance. Our findings indicate that hMT+/V5 is a region that can be modulated by focused imagery and that a specific cortico-cerebellar circuit is recruited during visual motion imagery leading to successful neurofeedback. These findings contribute to the debate on the relative potential of extrinsic (sensory) versus intrinsic (default-mode) brain regions in the clinical application of neurofeedback paradigms. This novel circuit might be a good target for future neurofeedback approaches that aim, for example, the training of focused attention in disorders such as ADHD.

Objective. Prefrontal hemodynamic responses are observed during performance of motor tasks. Using a dance video game (DVG), a complex motor task that requires temporally accurate footsteps with given visual and auditory cues, we investigated whether 20 h of DVG training modified hemodynamic responses of the prefrontal cortex in six healthy young adults. Approach. Fronto-temporal activity during actual DVG play was measured using functional near-infrared spectroscopy (fNIRS) pre- and post-training. To evaluate the training-induced changes in the time-courses of fNIRS signals, we employed a regression analysis using the task-specific template fNIRS signals that were generated from alternate well-trained and/or novice DVG players. The HRF was also separately incorporated as a template to construct an alternate regression model. Change in coefficients for template functions at pre- and post- training were determined and compared among different models. Main results. Training significantly increased the motor performance using the number of temporally accurate steps in the DVG as criteria. The mean oxygenated hemoglobin (ΔoxyHb) waveform changed from an activation above baseline pattern to that of a below baseline pattern. Participants showed significantly decreased coefficients for regressors of the ΔoxyHb response of novice players and HRF. The model using ΔoxyHb responses from both well-trained and novice players of DVG as templates showed the best fit for the ΔoxyHb responses of the participants at both pre- and post-training when analyzed with Akaike information criteria. Significance. These results suggest that the coefficients for the template ΔoxyHb responses of the novice players are sensitive indicators of motor learning during the initial stage of training and thus clinically useful to determine the improvement in motor performance when patients are engaged in a specific rehabilitation program.

Objective. Speech intelligibility with existing multichannel cochlear implants (CIs) is thought to be limited by poor spatial selectivity and interactions between CI channels caused by overlapping activation with monopolar (MP) stimulation. Our previous studies have shown that focused multipolar (FMP) and tripolar (TP) stimulation produce more restricted neural activation in the inferior colliculus (IC), compared to MP stimulation. Approach. This study explored interactions in the IC produced by simultaneous stimulation of two CI channels. We recorded multi-unit neural activity in the IC of anaesthetized cats with normal and severely degenerated spiral ganglion neuron populations in response to FMP, TP and MP stimulation from a 14 channel CI. Stimuli were applied to a 'fixed' CI channel, chosen toward the middle of the cochlear electrode array, and the effects of simultaneously stimulating a more apical 'test' CI channel were measured as a function of spatial separation between the two stimulation channels and stimulus level of the fixed channel. Channel interactions were quantified by changes in neural responses and IC threshold (i.e., threshold shift) elicited by simultaneous stimulation of two CI channels, compared to stimulation of the test channel alone. Main results. Channel interactions were significantly lower for FMP and TP than for MP stimulation (p < 0.001), whereas no significant difference was observed between FMP and TP stimulation. With MP stimulation, threshold shifts increased with decreased inter-electrode spacing and increased stimulus levels of the fixed channel. For FMP and TP stimulation, channel interactions were found to be similar for different inter-electrode spacing and stimulus levels of the fixed channel. Significance. The present study demonstrates how the degree of channel interactions in a CI can be controlled using stimulation configurations such as FMP and TP; such knowledge is essential in enhancing CI function in complex acoustic environments.

Objective. The ability of an automobile to infer the driver's upcoming actions directly from neural signals could enrich the interaction of the car with its driver. Intelligent vehicles fitted with an on-board brain–computer interface able to decode the driver's intentions can use this information to improve the driving experience. In this study we investigate the neural signatures of anticipation of specific actions, namely braking and accelerating. Approach. We investigated anticipatory slow cortical potentials in electroencephalogram recorded from 18 healthy participants in a driving simulator using a variant of the contingent negative variation (CNV) paradigm with Go and No-go conditions: count-down numbers followed by 'Start'/'Stop' cue. We report decoding performance before the action onset using a quadratic discriminant analysis classifier based on temporal features. Main results. (i) Despite the visual and driving related cognitive distractions, we show the presence of anticipatory event related potentials locked to the stimuli onset similar to the widely reported CNV signal (with an average peak value of −8 μV at electrode Cz). (ii) We demonstrate the discrimination between cases requiring to perform an action upon imperative subsequent stimulus (Go condition, e.g. a 'Red' traffic light) versus events that do not require such action (No-go condition; e.g. a 'Yellow' light); with an average single trial classification performance of 0.83 ± 0.13 for braking and 0.79 ± 0.12 for accelerating (area under the curve). (iii) We show that the centro-medial anticipatory potentials are observed as early as 320 ± 200 ms before the action with a detection rate of 0.77 ± 0.12 in offline analysis. Significance. We show for the first time the feasibility of predicting the driver's intention through decoding anticipatory related potentials during simulated car driving with high recognition rates.

Objective. Neural systems are comprised of interacting units, and relevant information regarding their function or malfunction can be inferred by analyzing the statistical dependencies between the activity of each unit. While correlations and mutual information are commonly used to characterize these dependencies, our objective here is to extend interactions to triplets of variables to better detect and characterize dynamic information transfer. Approach. Our approach relies on the measure of interaction information (II). The sign of II provides information as to the extent to which the interaction of variables in triplets is redundant (R) or synergetic (S). Three variables are said to be redundant when a third variable, say Z, added to a pair of variables (X, Y), diminishes the information shared between X and Y. Similarly, the interaction in the triplet is said to be synergetic when conditioning on Z enhances the information shared between X and Y with respect to the unconditioned state. Here, based on this approach, we calculated the R and S status for triplets of electrophysiological data recorded from drug-resistant patients with mesial temporal lobe epilepsy in order to study the spatial organization and dynamics of R and S close to the epileptogenic zone (the area responsible for seizure propagation). Main results. In terms of spatial organization, our results show that R matched the epileptogenic zone while S was distributed more in the surrounding area. In relation to dynamics, R made the largest contribution to high frequency bands (14–100 Hz), while S was expressed more strongly at lower frequencies (1–7 Hz). Thus, applying II to such clinical data reveals new aspects of epileptogenic structure in terms of the nature (redundancy versus synergy) and dynamics (fast versus slow rhythms) of the interactions. Significance. We expect this methodology, robust and simple, can reveal new aspects beyond pair-interactions in networks of interacting units in other setups with multi-recording data sets (and thus, not necessarily in epilepsy, the pathology we have approached here).

Objective. The majority of near-infrared spectroscopy (NIRS) brain–computer interface (BCI) studies have investigated binary classification problems. Limited work has considered differentiation of more than two mental states, or multi-class differentiation of higher-level cognitive tasks using measurements outside of the anterior prefrontal cortex. Improvements in accuracies are needed to deliver effective communication with a multi-class NIRS system. We investigated the feasibility of a ternary NIRS-BCI that supports mental states corresponding to verbal fluency task (VFT) performance, Stroop task performance, and unconstrained rest using prefrontal and parietal measurements. Approach. Prefrontal and parietal NIRS signals were acquired from 11 able-bodied adults during rest and performance of the VFT or Stroop task. Classification was performed offline using bagging with a linear discriminant base classifier trained on a 10 dimensional feature set. Main results. VFT, Stroop task and rest were classified at an average accuracy of 71.7% ± 7.9%. The ternary classification system provided a statistically significant improvement in information transfer rate relative to a binary system controlled by either mental task (0.87 ± 0.35 bits/min versus 0.73 ± 0.24 bits/min). Significance. These results suggest that effective communication can be achieved with a ternary NIRS-BCI that supports VFT, Stroop task and rest via measurements from the frontal and parietal cortices. Further development of such a system is warranted. Accurate ternary classification can enhance communication rates offered by NIRS-BCIs, improving the practicality of this technology.

Objective. A brain–computer interface (BCI) usually requires a time-consuming training phase during which data are collected and used to generate a classifier. Because brain signals vary dynamically over time (and even over sessions), this training phase may be necessary each time the BCI system is used, which is impractical. However, the variability in background noise, which is less dependent on a control signal, may dominate the dynamics of brain signals. Therefore, we hypothesized that an understanding of variations in background noise may allow existing data to be reused by incorporating the noise characteristics into the feature extraction framework; in this way, new session data are not required each time and this increases the feasibility of the BCI systems. Approach. In this work, we collected background noise during a single, brief on-site acquisition session (approximately 3 min) immediately before a new session, and we found that variations in background noise were predictable to some extent. Then we implemented this simple session-to-session transfer strategy with a regularized spatiotemporal filter (RSTF), and we tested it with a total of 20 cross-session datasets collected over multiple days from 12 subjects. We also proposed and tested a bias correction (BC) in the RSTF. Main results. We found that our proposed session-to-session strategies yielded a slightly less or comparable performance to the conventional paradigm (each session training phase is needed with an on-site training dataset). Furthermore, using an RSTF only and an RSTF with a BC outperformed existing approaches in session-to-session transfers. Significance. We inferred from our results that, with an on-site background noise suppression feature extractor and pre-existing training data, further training time may be unnecessary.

Objective. It is possible to develop implantable microstimulators whose actuation principle is based on rectification of high-frequency (HF) current bursts supplied through skin electrodes. This has been demonstrated previously by means of devices consisting of a single diode. However, previous single diode devices caused dc currents which made them impractical for clinical applications. Here flexible thread-like stimulation implants which perform charge balance are demonstrated in vivo. Approach. The implants weigh 40.5 mg and they consist of a 3 cm long tubular silicone body with a diameter of 1 mm, two electrodes at opposite ends, and, within the central section of the body, an electronic circuit made up of a diode, two capacitors, and a resistor. In the present study, each implant was percutaneously introduced through a 14 G catheter into either the gastrocnemius muscle or the cranial tibial muscle of a rabbit hindlimb. Then stimulation was performed by delivering HF bursts (amplitude <60 V, frequency 1 MHz, burst repetition frequency from 10 Hz to 200 Hz, duration = 200 μs) through a pair of textile electrodes strapped around the hindlimb and either isometric plantarflexion or dorsiflexion forces were recorded. Stimulation was also assayed 1, 2 and 4 weeks after implantation. Main results. The implants produced bursts of rectified current whose mean value was of a few mA and were capable of causing local neuromuscular stimulation. The implants were well-tolerated during the 4 weeks. Significance. Existing power supply methods, and, in particular inductive links, comprise stiff and bulky parts. This hinders the development of minimally invasive implantable devices for neuroprostheses based on electrical stimulation. The proposed methodology is intended to relieving such bottleneck. In terms of mass, thinness, and flexibility, the demonstrated implants appear to be unprecedented among the intramuscular stimulation implants ever assayed in vertebrates.

Objective. We consider the problem of optimal probing to learn connections in an evoked dynamic network. Such a network, in which each edge measures an input–output relationship between sites in sensor/actuator-space, is relevant to emerging applications in neural mapping and neural connectivity estimation. Approach. We show that the problem of scheduling nodes to a probe (i.e., stimulate) amounts to a problem of optimal sensor scheduling. Main results. By formulating the evoked network in state-space, we show that the solution to the greedy probing strategy has a convenient form and, under certain conditions, is optimal over a finite horizon. We adopt an expectation maximization technique to update the state-space parameters in an online fashion and demonstrate the efficacy of the overall approach in a series of detailed numerical examples. Significance. The proposed method provides a principled means to actively probe time-varying connections in neuronal networks. The overall method can be implemented in real time and is particularly well-suited to applications in stimulation-based cortical mapping in which the underlying network dynamics are changing over time.

Objective. Using the Medtronic Activa® PC + S system, this study investigated how passive joint manipulation, reaching behavior, and deep brain stimulation (DBS) modulate local field potential (LFP) activity in the subthalamic nucleus (STN) and globus pallidus (GP). Approach. Five non-human primates were implanted unilaterally with one or more DBS leads. LFPs were collected in montage recordings during resting state conditions and during motor tasks that facilitate the expression of parkinsonian motor signs. These recordings were made in the naïve state in one subject, in the parkinsonian state in two subjects, and in both naïve and parkinsonian states in two subjects. Main results. LFPs measured at rest were consistent over time for a given recording location and parkinsonian state in a given subject; however, LFPs were highly variable between subjects, between and within recording locations, and across parkinsonian states. LFPs in both naïve and parkinsonian states across all recorded nuclei contained a spectral peak in the beta band (10–30 Hz). Moreover, the spectral content of recorded LFPs was modulated by passive and active movement of the subjects' limbs. LFPs recorded during a cued-reaching task displayed task-related beta desynchronization in STN and GP. The bidirectional capabilities of the Activa® PC + S also allowed for recording LFPs while delivering DBS. The therapeutic effect of STN DBS on parkinsonian rigidity outlasted stimulation for 30–60 s, but there was no correlation with beta band power. Significance. This study emphasizes (1) the variability in spontaneous LFPs amongst subjects and (2) the value of using the Activa® PC + S system to record neural data in the context of behavioral tasks that allow one to evaluate a subject's symptomatology.

Objective. Deep brain stimulation (DBS) has become the standard treatment for advanced stages of Parkinson's disease (PD) and other motor disorders. Although the surgical procedure has improved in accuracy over the years thanks to imaging and microelectrode recordings, the underlying principles that render DBS effective are still debated today. The aim of this paper is to present initial findings around a new biomarker that is capable of assessing the efficacy of DBS treatment for PD which could be used both as a research tool, as well as in the context of a closed-loop stimulator. Approach. We have used a novel multi-channel stimulator and recording device capable of measuring the response of nervous tissue to stimulation very close to the stimulus site with minimal latency, rejecting most of the stimulus artefact usually found with commercial devices. We have recorded and analyzed the responses obtained intraoperatively in two patients undergoing DBS surgery in the subthalamic nucleus (STN) for advanced PD. Main results. We have identified a biomarker in the responses of the STN to DBS. The responses can be analyzed in two parts, an initial evoked compound action potential arising directly after the stimulus onset, and late responses (LRs), taking the form of positive peaks, that follow the initial response. We have observed a morphological change in the LRs coinciding with a decrease in the rigidity of the patients. Significance. These initial results could lead to a better characterization of the DBS therapy, and the design of adaptive DBS algorithms that could significantly improve existing therapies and help us gain insights into the functioning of the basal ganglia and DBS.

Objective. Representation of movement in the motor cortex (M1) has been widely studied in brain–machine interfaces (BMIs). The electromyogram (EMG) has greater bandwidth than the conventional kinematic variables (such as position, velocity), and is functionally related to the discharge of cortical neurons. As the stochastic information of EMG is derived from the explicit spike time structure, point process (PP) methods will be a good solution for decoding EMG directly from neural spike trains. Previous studies usually assume linear or exponential tuning curves between neural firing and EMG, which may not be true. Approach. In our analysis, we estimate the tuning curves in a data-driven way and find both the traditional functional-excitatory and functional-inhibitory neurons, which are widely found across a rat's motor cortex. To accurately decode EMG envelopes from M1 neural spike trains, the Monte Carlo point process (MCPP) method is implemented based on such nonlinear tuning properties. Main results. Better reconstruction of EMG signals is shown on baseline and extreme high peaks, as our method can better preserve the nonlinearity of the neural tuning during decoding. The MCPP improves the prediction accuracy (the normalized mean squared error) 57% and 66% on average compared with the adaptive point process filter using linear and exponential tuning curves respectively, for all 112 data segments across six rats. Compared to a Wiener filter using spike rates with an optimal window size of 50 ms, MCPP decoding EMG from a point process improves the normalized mean square error (NMSE) by 59% on average. Significance. These results suggest that neural tuning is constantly changing during task execution and therefore, the use of spike timing methodologies and estimation of appropriate tuning curves needs to be undertaken for better EMG decoding in motor BMIs.

Objective. Elucidation of neural activity underpinning rodent behaviour has traditionally been hampered by the use of tethered systems and human involvement. Furthermore the combination of deep-brain stimulation (DBS) and various neural recording modalities can lead to complex and time-consuming laboratory setups. For studies of this type, novel tools are required to drive forward this research. Approach. A miniature wireless system weighing 8.5 g (including battery) was developed for rodent use that combined multichannel DBS and local-field potential (LFP) recordings. Its performance was verified in a working memory task that involved 4-channel fronto-hippocampal LFP recording and bilateral constant-current fimbria-fornix DBS. The system was synchronised with video-tracking for extraction of LFP at discrete task phases, and DBS was activated intermittently at discrete phases of the task. Main results. In addition to having a fast set-up time, the system could reliably transmit continuous LFP at over 8 hours across 3–5 m distances. During the working memory task, LFP pertaining to discrete task phases was extracted and compared with well-known neural correlates of active exploratory behaviour in rodents. DBS could be wirelessly activated/deactivated at any part of the experiment during EEG recording and transmission, allowing for a seamless integration of this modality. Significance. The wireless system combines a small size with a level of robustness and versatility that can greatly simplify rodent behavioural experiments involving EEG recording and DBS. Designed for versatility and simplicity, the small size and low-cost of the system and its receiver allow for enhanced portability, fast experimental setup times, and pave the way for integration with more complex behaviour.

Objective. We quantitatively investigate the biocompatibility of chemical vapour deposited (CVD) nanocrystalline diamond (NCD) after the inclusion of boron, with and without nanostructuring. The nanostructuring method involves a novel approach of growing NCD over carbon nanotubes (CNTs) that act as a 3D scaffold. This nanostructuring of BNCD leads to a material with increased capacitance, and this along with wide electrochemical window makes BNCD an ideal material for neural interface applications, and thus it is essential that their biocompatibility is investigated. Approach. Biocompatibility was assessed by observing the interaction of human neural stem cells (hNSCs) with a variety of NCD substrates including un-doped ones, and NCD doped with boron, which are both planar, and nanostructured. hNSCs were chosen due to their sensitivity, and various methods including cell population and confluency were used to quantify biocompatibility. Main results. Boron inclusion into NCD film was shown to have no observable effect on hNSC attachment, proliferation and viability. Furthermore, the biocompatibility of nanostructured boron-doped NCD is increased upon nanostructuring, potentially due to the increased surface area. Significance. Diamond is an attractive material for supporting the attachment and development of cells as it can show exceptional biocompatibility. When boron is used as a dopant within diamond it becomes a p-type semiconductor, and at high concentrations the diamond becomes quasi-metallic, offering the prospect of a direct electrical device-cell interfacing system.

Objective. Steady-state visual evoked potentials (SSVEPs), the brain responses to repetitive visual stimulation (RVS), are widely utilized in neuroscience. Their high signal-to-noise ratio and ability to entrain oscillatory brain activity are beneficial for their applications in brain-computer interfaces, investigation of neural processes underlying brain rhythmic activity (steady-state topography) and probing the causal role of brain rhythms in cognition and emotion. This paper aims at analyzing the space and time EEG dynamics in response to RVS at the frequency of stimulation and ongoing rhythms in the delta, theta, alpha, beta, and gamma bands. Approach.We used electroencephalography (EEG) to study the oscillatory brain dynamics during RVS at 10 frequencies in the gamma band (40–60 Hz). We collected an extensive EEG data set from 32 participants and analyzed the RVS evoked and induced responses in the time-frequency domain. Main results. Stable SSVEP over parieto-occipital sites was observed at each of the fundamental frequencies and their harmonics and sub-harmonics. Both the strength and the spatial propagation of the SSVEP response seem sensitive to stimulus frequency. The SSVEP was more localized around the parieto-occipital sites for higher frequencies (>54 Hz) and spread to fronto-central locations for lower frequencies. We observed a strong negative correlation between stimulation frequency and relative power change at that frequency, the first harmonic and the sub-harmonic components over occipital sites. Interestingly, over parietal sites for sub-harmonics a positive correlation of relative power change and stimulation frequency was found. A number of distinct patterns in delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz) and beta (15–30 Hz) bands were also observed. The transient response, from 0 to about 300 ms after stimulation onset, was accompanied by increase in delta and theta power over fronto-central and occipital sites, which returned to baseline after approx. 500 ms. During the steady-state response, we observed alpha band desynchronization over occipital sites and after 500 ms also over frontal sites, while neighboring areas synchronized. The power in beta band over occipital sites increased during the stimulation period, possibly caused by increase in power at sub-harmonic frequencies of stimulation. Gamma power was also enhanced by the stimulation. Significance. These findings have direct implications on the use of RVS and SSVEPs for neural process investigation through steady-state topography, controlled entrainment of brain oscillations and BCIs. A deep understanding of SSVEP propagation in time and space and the link with ongoing brain rhythms is crucial for optimizing the typical SSVEP applications for studying, assisting, or augmenting human cognitive and sensorimotor function.

Objective. One approach to conveying sensory feedback in neuroprostheses is to electrically stimulate sensory neurons in the cortex. For this approach to be viable, it is critical that intracortical microstimulation (ICMS) causes minimal damage to the brain. Here, we investigate the effects of chronic ICMS on the neuronal tissue across a variety of stimulation regimes in non-human primates. We also examine each animal's ability to use their hand—the cortical representation of which is targeted by the ICMS—as a further assay of possible neuronal damage. Approach. We implanted electrode arrays in the primary somatosensory cortex of three Rhesus macaques and delivered ICMS four hours per day, five days per week, for six months. Multiple regimes of ICMS were delivered to investigate the effects of stimulation parameters on the tissue and behavior. Parameters included current amplitude (10–100 μA), pulse train duration (1, 5 s), and duty cycle (1/1, 1/3). We then performed a range of histopathological assays on tissue near the tips of both stimulated and unstimulated electrodes to assess the effects of chronic ICMS on the tissue and their dependence on stimulation parameters. Main results. While the implantation and residence of the arrays in the cortical tissue did cause significant damage, chronic ICMS had no detectable additional effect; furthermore, the animals exhibited no impairments in fine motor control. Significance. Chronic ICMS may be a viable means to convey sensory feedback in neuroprostheses as it does not cause significant damage to the stimulated tissue.

Objective. The various parameters that define a hand movement such as its trajectory, speed, etc, are encoded in distinct brain activities. Decoding this information from neurophysiological recordings is a less explored area of brain–computer interface (BCI) research. Applying non-invasive recordings such as electroencephalography (EEG) for decoding makes the problem more challenging, as the encoding is assumed to be deep within the brain and not easily accessible by scalp recordings. Approach. EEG based BCI systems can be developed to identify the neural features underlying movement parameters that can be further utilized to provide a detailed and well defined control command set to a BCI output device. A real-time continuous control is better suited for practical BCI systems, and can be achieved by continuous adaptive reconstruction of movement trajectory than discrete brain activity classifications. In this work, we adaptively reconstruct/estimate the parameters of two-dimensional hand movement trajectory, namely movement speed and position, from multi-channel EEG recordings. The data for analysis is collected by performing an experiment that involved center-out right-hand movement tasks in four different directions at two different speeds in random order. We estimate movement trajectory using a Kalman filter that models the relation between brain activity and recorded parameters based on a set of defined predictors. We propose a method to define these predictor variables that includes spatial, spectral and temporally localized neural information and to select optimally informative variables. Main results. The proposed method yielded correlation of (0.60 ± 0.07) between recorded and estimated data. Further, incorporating the proposed predictor subset selection, the correlation achieved is (0.57 ± 0.07, p ${\lt }0.004)$ with significant gain in stability of the system, as well as dramatic reduction in number of predictors (76%) for the savings of computational time. Significance. The proposed system provides a real time movement control system using EEG-BCI with control over movement speed and position. These results are higher and statistically significant compared to existing techniques in EEG based systems and thus promise the applicability of the proposed method for efficient estimation of movement parameters and for continuous motor control.

Objective. Oscillations are an important aspect of brain activity, but they often have a low signal-to-noise ratio (SNR) due to source-to-electrode mixing with competing brain activity and noise. Filtering can improve the SNR of narrowband signals, but it introduces ringing effects that may masquerade as genuine oscillations, leading to uncertainty as to the true oscillatory nature of the phenomena. Likewise, time–frequency analysis kernels have a temporal extent that blurs the time course of narrowband activity, introducing uncertainty as to timing and causal relations between events and/or frequency bands. Approach. Here, we propose a methodology that reveals narrowband activity within multichannel data such as electroencephalography, magnetoencephalography, electrocorticography or local field potential. The method exploits the between-channel correlation structure of the data to suppress competing sources by joint diagonalization of the covariance matrices of narrowband filtered and unfiltered data. Main results. Applied to synthetic and real data, the method effectively extracts narrowband components at unfavorable SNR. Significance. Oscillatory components of brain activity, including weak sources that are hard or impossible to observe using standard methods, can be detected and their time course plotted accurately. The method avoids the temporal artifacts of standard filtering and time–frequency analysis methods with which it remains complementary.

Objective. Brain–computer interface (BCI) technology might contribute to rehabilitation of motor function. This speculation is based on the premise that modifying the electroencephalographic (EEG) activity will modify behavior, a proposition for which there is limited empirical data. The present study asked whether learned modulation of pre-movement sensorimotor rhythm (SMR) activity can affect motor performance in normal human subjects. Approach. Eight individuals first performed a joystick-based cursor-movement task with variable warning periods. Targets appeared randomly on a video monitor and subjects moved the cursor to the target and pressed a select button within 2 s. SMR features in the pre-movement EEG that correlated with performance speed and accuracy were identified. The subjects then learned to increase or decrease these features to control a two-target BCI task. Following successful BCI training, they were asked to increase or decrease SMR amplitude in order to initiate the joystick task. Main results. After BCI training, pre-movement SMR amplitude was correlated with performance in subjects with initial poor performance: lower amplitude was associated with faster and more accurate movement. The beneficial effect on performance of lower SMR amplitude was greater in subjects with lower initial performance levels. Significance. These results indicate that BCI-based SMR training can affect a standard motor behavior. They provide a rationale for studies that integrate such training into rehabilitation protocols and examine its capacity to enhance restoration of useful motor function.

Objective. Myoelectric activity volitionally generated by the user is often used for controlling hand prostheses in order to replicate the synergistic actions of muscles in healthy humans during grasping. Muscle synergies in healthy humans are based on the integration of visual perception, heuristics and proprioception. Here, we demonstrate how sensor fusion that combines artificial vision and proprioceptive information with the high-level processing characteristics of biological systems can be effectively used in transradial prosthesis control. Approach. We developed a novel context- and user-aware prosthesis (CASP) controller integrating computer vision and inertial sensing with myoelectric activity in order to achieve semi-autonomous and reactive control of a prosthetic hand. The presented method semi-automatically provides simultaneous and proportional control of multiple degrees-of-freedom (DOFs), thus decreasing overall physical effort while retaining full user control. The system was compared against the major commercial state-of-the art myoelectric control system in ten able-bodied and one amputee subject. All subjects used transradial prosthesis with an active wrist to grasp objects typically associated with activities of daily living. Main results. The CASP significantly outperformed the myoelectric interface when controlling all of the prosthesis DOF. However, when tested with less complex prosthetic system (smaller number of DOF), the CASP was slower but resulted with reaching motions that contained less compensatory movements. Another important finding is that the CASP system required minimal user adaptation and training. Significance. The CASP constitutes a substantial improvement for the control of multi-DOF prostheses. The application of the CASP will have a significant impact when translated to real-life scenarious, particularly with respect to improving the usability and acceptance of highly complex systems (e.g., full prosthetic arms) by amputees.

Objective. Simplified neuronal circuits are required for investigating information representation in nervous systems and for validating theoretical neural network models. Here, we developed patterned neuronal circuits using micro fabricated devices, comprising a micro-well array bonded to a microelectrode-array substrate. Approach. The micro-well array consisted of micrometre-scale wells connected by tunnels, all contained within a silicone slab called a micro-chamber. The design of the micro-chamber confined somata to the wells and allowed axons to grow through the tunnels bidirectionally but with a designed, unidirectional bias. We guided axons into the point of the arrow structure where one of the two tunnel entrances is located, making that the preferred direction. Main results. When rat cortical neurons were cultured in the wells, their axons grew through the tunnels and connected to neurons in adjoining wells. Unidirectional burst transfers and other asymmetric signal-propagation phenomena were observed via the substrate-embedded electrodes. Seventy-nine percent of burst transfers were in the forward direction. We also observed rapid propagation of activity from sites of local electrical stimulation, and significant effects of inhibitory synapse blockade on bursting activity. Significance. These results suggest that this simple, substrate-controlled neuronal circuit can be applied to develop in vitro models of the function of cortical microcircuits or deep neural networks, better to elucidate the laws governing the dynamics of neuronal networks.

Objective. Motor imagery-based brain–computer interface (MI-BCI) systems hold promise in motor function rehabilitation and assistance for motor function impaired people. But the ability to operate an MI-BCI varies across subjects, which becomes a substantial problem for practical BCI applications beyond the laboratory. Approach. Several previous studies have demonstrated that individual MI-BCI performance is related to the resting state of brain. In this study, we further investigate offline MI-BCI performance variations through the perspective of resting-state electroencephalography (EEG) network. Main results. Spatial topologies and statistical measures of the network have close relationships with MI classification accuracy. Specifically, mean functional connectivity, node degrees, edge strengths, clustering coefficient, local efficiency and global efficiency are positively correlated with MI classification accuracy, whereas the characteristic path length is negatively correlated with MI classification accuracy. The above results indicate that an efficient background EEG network may facilitate MI-BCI performance. Finally, a multiple linear regression model was adopted to predict subjects' MI classification accuracy based on the efficiency measures of the resting-state EEG network, resulting in a reliable prediction. Significance. This study reveals the network mechanisms of the MI-BCI and may help to find new strategies for improving MI-BCI performance.

Objective. Determining the means by which perceived stimuli are compactly represented in the human brain is a difficult task. This study aimed to develop techniques for the construction of the neural manifold as a representation of visual stimuli. Approach. We propose a supervised locally linear embedding method to construct the embedded manifold from brain activity, taking into account similarities between corresponding stimuli. In our experiments, photographic portraits were used as visual stimuli and brain activity was calculated from magnetoencephalographic data using a source localization method. Main results. The results of 10 × 10-fold cross-validation revealed a strong correlation between manifolds of brain activity and the orientation of faces in the presented images, suggesting that high-level information related to image content can be revealed in the brain responses represented in the manifold. Significance. Our experiments demonstrate that the proposed method is applicable to investigation into the inherent patterns of brain activity.

Objective. A brain–computer interface (BCI) is an interface that uses signals from the brain to control a computer. BCIs will likely become important tools for severely paralyzed patients to restore interaction with the environment. The sensorimotor cortex is a promising target brain region for a BCI due to the detailed topography and minimal functional interference with other important brain processes. Previous studies have shown that attempted movements in paralyzed people generate neural activity that strongly resembles actual movements. Hence decodability for BCI applications can be studied in able-bodied volunteers with actual movements. Approach. In this study we tested whether mouth movements provide adequate signals in the sensorimotor cortex for a BCI. The study was executed using fMRI at 7 T to ensure relevance for BCI with cortical electrodes, as 7 T measurements have been shown to correlate well with electrocortical measurements. Twelve healthy volunteers executed four mouth movements (lip protrusion, tongue movement, teeth clenching, and the production of a larynx activating sound) while in the scanner. Subjects performed a training and a test run. Single trials were classified based on the Pearson correlation values between the activation patterns per trial type in the training run and single trials in the test run in a 'winner-takes-all' design. Main results. Single trial mouth movements could be classified with 90% accuracy. The classification was based on an area with a volume of about 0.5 cc, located on the sensorimotor cortex. If voxels were limited to the surface, which is accessible for electrode grids, classification accuracy was still very high (82%). Voxels located on the precentral cortex performed better (87%) than the postcentral cortex (72%). Significance. The high reliability of decoding mouth movements suggests that attempted mouth movements are a promising candidate for BCI in paralyzed people.

Objective. State of the art brain–computer interface (BCI) research focuses on improving individual components such as the application or the decoder that converts the user's brain activity to control signals. In this study, we investigate the interaction between these components in the P300 speller, a BCI for communication. We introduce a synergistic approach in which the stimulus presentation sequence is modified to enhance the machine learning decoding. In this way we aim for an improved overall BCI performance. Approach. First, a new stimulus presentation paradigm is introduced which provides us flexibility in tuning the sequence of visual stimuli presented to the user. Next, an experimental setup in which this paradigm is compared to other paradigms uncovers the underlying mechanism of the interdependence between the application and the performance of the decoder. Main results. Extensive analysis of the experimental results reveals the changing requirements of the decoder concerning the data recorded during the spelling session. When few data is recorded, the balance in the number of target and non-target stimuli shown to the user is more important than the signal-to-noise rate (SNR) of the recorded response signals. Only when more data has been collected, the SNR becomes the dominant factor. Significance. For BCIs in general, knowing the dominant factor that affects the decoder performance and being able to respond to it is of utmost importance to improve system performance. For the P300 speller, the proposed tunable paradigm offers the possibility to tune the application to the decoder's needs at any time and, as such, fully exploit this application-decoder interaction.

Objectives. Recent studies have started to explore the implementation of brain–computer interfaces (BCI) as part of driving assistant systems. The current study presents an EEG-based BCI that decodes error-related brain activity. Such information can be used, e.g., to predict driver's intended turning direction before reaching road intersections. Approach. We executed experiments in a car simulator (N = 22) and a real car (N = 8). While subject was driving, a directional cue was shown before reaching an intersection, and we classified the presence or not of an error-related potentials from EEG to infer whether the cued direction coincided with the subject's intention. In this protocol, the directional cue can correspond to an estimation of the driving direction provided by a driving assistance system. We analyzed ERPs elicited during normal driving and evaluated the classification performance in both offline and online tests. Results. An average classification accuracy of 0.698 ± 0.065 was obtained in offline experiments in the car simulator, while tests in the real car yielded a performance of 0.682 ± 0.059. The results were significantly higher than chance level for all cases. Online experiments led to equivalent performances in both simulated and real car driving experiments. These results support the feasibility of decoding these signals to help estimating whether the driver's intention coincides with the advice provided by the driving assistant in a real car. Significance. The study demonstrates a BCI system in real-world driving, extending the work from previous simulated studies. As far as we know, this is the first online study in real car decoding driver's error-related brain activity. Given the encouraging results, the paradigm could be further improved by using more sophisticated machine learning approaches and possibly be combined with applications in intelligent vehicles.

Objective. Nanoporous gold (Au) structures can reduce the impedance and enhance the charge injection capability of multi-electrode arrays (MEAs) used for interfacing neuronal networks. Even though there are various nanoporous Au preparation techniques, fabrication of MEA based on low-cost electro-codeposition of Ag:Au has not been performed. In this work, we have modified a Au MEA via the electro-codeposition of Ag:Au alloy, followed by the chemical etching of Ag, and report on the in vitro extracellular recording and stimulation performance of the nanoporous Au-modified MEA. Approach. Ag:Au alloy was electro-codeposited on a bilayer lift-off resist sputter-deposition passivated Au MEA followed by chemical etching of Ag to form a porous Au structure. Main results. The porous Au structure was analyzed by scanning electron microscopy and tunneling electron microscopy and found to have an interconnected nanoporous Au structure. The impedance value of the nanoporous Au-modified MEA is 15.4 ± 0.55 kΩ at 1 kHz, accompanied by the base noise V_rms of 2.4 ± 0.3 μV. The charge injection limit of the nanoporous Au-modified electrode estimated from voltage transient measurement is approximately 1 mC cm⁻², which is comparable to roughened platinum and carbon nanotube electrodes. The charge injection capability of the nanoporous Au-modified MEA was confirmed by observing stimulus-induced spikes at above 0.2 V. The nanoporous Au-modified MEA showed mechanical durability upon ultrasonic treatment for up to an hour. Significance. Electro-codeposition of Ag:Au alloy combined with chemical etching Ag is a low-cost process for fabricating nanoporous Au-modified MEA suitable for establishing the stimulus–response relationship of cultured neuronal networks.

Objective. Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Approach. Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts' law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Main results. Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p < 0.05) by preventing extraneous movement at additional DOFs. Similar results were seen in experiments with two transradial amputees. Though goodness-of-fit evaluations suggested that the EMG feature distributions showed some deviations from the Gaussian, equal-covariance assumptions used in this experiment, the assumptions were sufficiently met to provide improved performance compared to linear regression control. Significance. Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.