Journal of Neural Engineering

Objective. Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain–computer interfaces, BCI's), and even commercial applications. Many of the analytical tools used in EEG studies have used machine learning to uncover relevant information for neural classification and neuroimaging. Recently, the availability of large EEG data sets and advances in machine learning have both led to the deployment of deep learning architectures, especially in the analysis of EEG signals and in understanding the information it may contain for brain functionality. The robust automatic classification of these signals is an important step towards making the use of EEG more practical in many applications and less reliant on trained professionals. Towards this goal, a systematic review of the literature on deep learning applications to EEG classification was performed to address the following critical questions: (1) Which EEG classification tasks have been explored with deep learning? (2) What input formulations have been used for training the deep networks? (3) Are there specific deep learning network structures suitable for specific types of tasks? Approach. A systematic literature review of EEG classification using deep learning was performed on Web of Science and PubMed databases, resulting in 90 identified studies. Those studies were analyzed based on type of task, EEG preprocessing methods, input type, and deep learning architecture. Main results. For EEG classification tasks, convolutional neural networks, recurrent neural networks, deep belief networks outperform stacked auto-encoders and multi-layer perceptron neural networks in classification accuracy. The tasks that used deep learning fell into five general groups: emotion recognition, motor imagery, mental workload, seizure detection, event related potential detection, and sleep scoring. For each type of task, we describe the specific input formulation, major characteristics, and end classifier recommendations found through this review. Significance. This review summarizes the current practices and performance outcomes in the use of deep learning for EEG classification. Practical suggestions on the selection of many hyperparameters are provided in the hope that they will promote or guide the deployment of deep learning to EEG datasets in future research.

Objective. This study examined the strength and stability of motor signals in low gamma and high gamma bands of vascular electrocorticograms (vECoG) recorded with endovascular stent-electrode arrays (Stentrodes) implanted in the superior sagittal sinus of two participants with severe paralysis due to amyotrophic lateral sclerosis. Approach. vECoG signals were recorded from two participants in the COMMAND trial, an Early Feasibility Study of the Stentrode brain–computer interface (BCI) (NCT05035823). The participants performed attempted movements of their ankles or hands. The signals were band-pass filtered to isolate low gamma (30–70 Hz) and high gamma (70–200 Hz) components. The strength of vECoG motor activity was measured as signal-to-noise ratio (SNR) and the percentage change in signal amplitude between the rest and attempted movement epochs, which we termed depth of modulation (DoM). We trained and tested classifiers to evaluate the accuracy and stability of detecting motor intent. Main results. Both low gamma and high gamma were modulated during attempted movements. For Participant 1, the average DoM across channels and sessions was 125.41 ± 17.53% for low gamma and 54.23 ± 4.52% for high gamma, with corresponding SNR values of 6.75 ± 0.37 dB and 3.69 ± 0.28 dB. For Participant 2, the average DoM was 22.77 ± 4.09% for low gamma and 22.53 ± 2.04% for high gamma, with corresponding SNR values of 1.72 ± 0.25 dB and 1.73 ± 0.13 dB. vECoG amplitudes remained significantly different between rest and move periods over the 3 month testing period, with >90% accuracy in discriminating attempted movement from rest epochs for both participants. For Participant 1, the average DoM was strongest during attempted movements of both ankles, while for Participant 2, the DoM was greatest for attempted movement of the right hand. The overall classification accuracy was 91.43% for Participant 1 and 70.37% for Participant 2 in offline decoding of multiple attempted movements and rest conditions. Significance. By eliminating the need for open brain surgery, the Stentrode offers a promising BCI alternative, potentially enhancing access to BCIs for individuals with severe motor impairments. This study provides preliminary evidence that the Stentrode can detect discriminable signals indicating motor intent, with motor signal modulation observed over the 3 month testing period reported here.

Context. Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question. Objective. In this work, we review 154 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain–computer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations. Methods. Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to (1) the data, (2) the preprocessing methodology, (3) the DL design choices, (4) the results, and (5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. Results. Our analysis reveals that the amount of EEG data used across studies varies from less than ten minutes to thousands of hours, while the number of samples seen during training by a network varies from a few dozens to several millions, depending on how epochs are extracted. Interestingly, we saw that more than half the studies used publicly available data and that there has also been a clear shift from intra-subject to inter-subject approaches over the last few years. About of the studies used convolutional neural networks (CNNs), while used recurrent neural networks (RNNs), most often with a total of 3–10 layers. Moreover, almost one-half of the studies trained their models on raw or preprocessed EEG time series. Finally, the median gain in accuracy of DL approaches over traditional baselines was across all relevant studies. More importantly, however, we noticed studies often suffer from poor reproducibility: a majority of papers would be hard or impossible to reproduce given the unavailability of their data and code. Significance. To help the community progress and share work more effectively, we provide a list of recommendations for future studies and emphasize the need for more reproducible research. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly. A planned follow-up to this work will be an online public benchmarking portal listing reproducible results.

Objective. Localization of function within the brain and central nervous system is an essential aspect of clinical neuroscience. Classical descriptions of functional neuroanatomy provide a foundation for understanding the functional significance of identifiable anatomic structures. However, individuals exhibit substantial variation, particularly in the presence of disorders that alter tissue structure or impact function. Furthermore, functional regions do not always correspond to identifiable structural features. Understanding function at the level of individual patients—and diagnosing and treating such patients—often requires techniques capable of correlating neural activity with cognition, behavior, and experience in anatomically precise ways. Approach. Recent advances in brain–computer interface technology have given rise to a new generation of electrophysiologic tools for scalable, nondestructive functional mapping with spatial precision in the range of tens to hundreds of micrometers, and temporal resolutions in the range of tens to hundreds of microseconds. Here we describe our initial intraoperative experience with novel, thin-film arrays containing 1024 surface microelectrodes for electrocorticographic mapping in a first-in-human study. Main results. Eight patients undergoing standard electrophysiologic cortical mapping during resection of eloquent-region brain tumors consented to brief sessions of concurrent mapping (micro-electrocorticography) using the novel arrays. Four patients underwent motor mapping using somatosensory evoked potentials (SSEPs) while under general anesthesia, and four underwent awake language mapping, using both standard paradigms and the novel microelectrode array. SSEP phase reversal was identified in the region predicted by conventional mapping, but at higher resolution (0.4 mm) and as a contour rather than as a point. In Broca's area (confirmed by direct cortical stimulation), speech planning was apparent in the micro-electrocorticogram as high-amplitude beta-band activity immediately prior to the articulatory event. Significance. These findings support the feasibility and potential clinical utility of incorporating micro-electrocorticography into the intraoperative workflow for systematic cortical mapping of functional brain regions.

Objective. Most current electroencephalography (EEG)-based brain–computer interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately ten years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs. Approach. We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs. We synthesize these studies in order to present such algorithms, to report how they were used for BCIs, what were the outcomes, and to identify their pros and cons. Main results. We found that the recently designed classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, plus a few other miscellaneous classifiers. Among these, adaptive classifiers were demonstrated to be generally superior to static ones, even with unsupervised adaptation. Transfer learning can also prove useful although the benefits of transfer learning remain unpredictable. Riemannian geometry-based methods have reached state-of-the-art performances on multiple BCI problems and deserve to be explored more thoroughly, along with tensor-based methods. Shrinkage linear discriminant analysis and random forests also appear particularly useful for small training samples settings. On the other hand, deep learning methods have not yet shown convincing improvement over state-of-the-art BCI methods. Significance. This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these methods and guidelines on when and how to use them. It also identifies a number of challenges to further advance EEG classification in BCI.

Objective. Brain–computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI paradigm, feature extractors and classifiers are tailored to the distinct characteristics of its expected EEG control signal, limiting its application to that specific signal. Convolutional neural networks (CNNs), which have been used in computer vision and speech recognition to perform automatic feature extraction and classification, have successfully been applied to EEG-based BCIs; however, they have mainly been applied to single BCI paradigms and thus it remains unclear how these architectures generalize to other paradigms. Here, we ask if we can design a single CNN architecture to accurately classify EEG signals from different BCI paradigms, while simultaneously being as compact as possible. Approach. In this work we introduce EEGNet, a compact convolutional neural network for EEG-based BCIs. We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI. We compare EEGNet, both for within-subject and cross-subject classification, to current state-of-the-art approaches across four BCI paradigms: P300 visual-evoked potentials, error-related negativity responses (ERN), movement-related cortical potentials (MRCP), and sensory motor rhythms (SMR). Main results. We show that EEGNet generalizes across paradigms better than, and achieves comparably high performance to, the reference algorithms when only limited training data is available across all tested paradigms. In addition, we demonstrate three different approaches to visualize the contents of a trained EEGNet model to enable interpretation of the learned features. Significance. Our results suggest that EEGNet is robust enough to learn a wide variety of interpretable features over a range of BCI tasks. Our models can be found at: https://github.com/vlawhern/arl-eegmodels.

Stroke is one of the leading causes of long-term disability among adults and contributes to major socio-economic burden globally. Stroke frequently results in multifaceted impairments including motor, cognitive and emotion deficits. In recent years, brain–computer interface (BCI)-based therapy has shown promising results for post-stroke motor rehabilitation. In spite of the success received by BCI-based interventions in the motor domain, non-motor impairments are yet to receive similar attention in research and clinical settings. Some preliminary encouraging results in post-stroke cognitive rehabilitation using BCI seem to suggest that it may also hold potential for treating non-motor deficits such as cognitive and emotion impairments. Moreover, past studies have shown an intricate relationship between motor, cognitive and emotion functions which might influence the overall post-stroke rehabilitation outcome. A number of studies highlight the inability of current treatment protocols to account for the implicit interplay between motor, cognitive and emotion functions. This indicates the necessity to explore an all-inclusive treatment plan targeting the synergistic influence of these standalone interventions. This approach may lead to better overall recovery than treating the individual deficits in isolation. In this paper, we review the recent advances in BCI-based post-stroke motor rehabilitation and highlight the potential for the use of BCI systems beyond the motor domain, in particular, in improving cognition and emotion of stroke patients. Building on the current results and findings of studies in individual domains, we next discuss the possibility of a holistic BCI system for motor, cognitive and affect rehabilitation which may synergistically promote restorative neuroplasticity. Such a system would provide an all-encompassing rehabilitation platform, leading to overarching clinical outcomes and transfer of these outcomes to a better quality of living. This is one of the first works to analyse the possibility of targeting cross-domain influence of post-stroke functional recovery enabled by BCI-based rehabilitation.

Neuromorphic computing covers a diverse range of approaches to information processing all of which demonstrate some degree of neurobiological inspiration that differentiates them from mainstream conventional computing systems. The philosophy behind neuromorphic computing has its origins in the seminal work carried out by Carver Mead at Caltech in the late 1980s. This early work influenced others to carry developments forward, and advances in VLSI technology supported steady growth in the scale and capability of neuromorphic devices. Recently, a number of large-scale neuromorphic projects have emerged, taking the approach to unprecedented scales and capabilities. These large-scale projects are associated with major new funding initiatives for brain-related research, creating a sense that the time and circumstances are right for progress in our understanding of information processing in the brain. In this review we present a brief history of neuromorphic engineering then focus on some of the principal current large-scale projects, their main features, how their approaches are complementary and distinct, their advantages and drawbacks, and highlight the sorts of capabilities that each can deliver to neural modellers.

Objective. Patients undergoing general anesthesia may awaken and become aware of the surgical procedure. Due to neuromuscular blocking agents, patients could be conscious yet unable to move. Using brain–computer interface (BCI) technology, it may be possible to detect movement attempts from the EEG. However, it is unknown how an anesthetic influences the brain response to motor tasks. Approach. We tested the offline classification performance of a movement-based BCI in 12 healthy subjects at two effect-site concentrations of propofol. For each subject a second classifier was trained on the subject's data obtained before sedation, then tested on the data obtained during sedation ('transfer classification'). Main results. At concentration 0.5 μg ml⁻¹, despite an overall propofol EEG effect, the mean single trial classification accuracy was 85% (95% CI 81%–89%), and 83% (79%–88%) for the transfer classification. At 1.0 μg ml⁻¹, the accuracies were 81% (76%–86%), and 72% (66%–79%), respectively. At the highest propofol concentration for four subjects, unlike the remaining subjects, the movement-related brain response had been largely diminished, and the transfer classification accuracy was not significantly above chance. These subjects showed a slower and more erratic task response, indicating an altered state of consciousness distinct from that of the other subjects. Significance. The results show the potential of using a BCI to detect intra-operative awareness and justify further development of this paradigm. At the same time, the relationship between motor responses and consciousness and its clinical relevance for intraoperative awareness requires further investigation.

Objective. Deep learning (DL) networks are increasingly attracting attention across various fields, including electroencephalography (EEG) signal processing. These models provide comparable performance to that of traditional techniques. At present, however, there is a lack of well-structured and standardized datasets with specific benchmark limit the development of DL solutions for EEG denoising. Approach. Here, we present EEGdenoiseNet, a benchmark EEG dataset that is suited for training and testing DL-based denoising models, as well as for performance comparisons across models. EEGdenoiseNet contains 4514 clean EEG segments, 3400 ocular artifact segments and 5598 muscular artifact segments, allowing users to synthesize contaminated EEG segments with the ground-truth clean EEG. Main results. We used EEGdenoiseNet to evaluate denoising performance of four classical networks (a fully-connected network, a simple and a complex convolution network, and a recurrent neural network). Our results suggested that DL methods have great potential for EEG denoising even under high noise contamination. Significance. Through EEGdenoiseNet, we hope to accelerate the development of the emerging field of DL-based EEG denoising. The dataset and code are available at https://github.com/ncclabsustech/EEGdenoiseNet.

Objective. Effective seizure prediction can reduce patient burden, improve clinical treatment accuracy, and lower healthcare costs. However, existing deep learning-based seizure prediction methods primarily rely on single models, which have limitations in feature extraction. This study aims to develop a hybrid model that integrates the advantages of convolutional neural networks (CNNs) and Transformer to enhance seizure prediction performance. Approach. We propose FusionXNet, a hybrid model inspired by CNNs and Transformer architectures, for seizure prediction. Specifically, we design a token synthesis unit to extract local features using convolution operations and capture global electroencephalography (EEG) representations via attention mechanisms. By merging local and global features extracted from the EEG segments, FusionXNet enhances feature representations, which are subsequently fed into a classifier for final seizure prediction. Main results. We evaluate the model on the publicly available Boston Children's Hospital and the Massachusetts Institute of Technology dataset, conducting segment-based and event-based experiments in both patient-specific and cross-patient settings. In event-based patient-specific experiments, FusionXNet achieves a sensitivity of 97.602% and a false positive rate (FPR) of 0.059 h⁻¹. The results demonstrate that the proposed model effectively predicts seizures with high sensitivity and a low FPR, outperforming existing methods. Significance. The proposed FusionXNet model provides a robust and efficient approach for seizure prediction by leveraging both local and global feature extraction. The high sensitivity and low FPR indicate its potential for real-world clinical applications, improving patient management and reducing healthcare costs.

Objective. To evaluate the effectiveness of a novel depth-based vision processing (VP) method, local background enclosure (LBE), in comparison to the comprehensive VP method, Lanczos2 (L2), in suprachoroidal retinal prosthesis implant recipients during navigational tasks in laboratory and real-world settings. Approach. Four participants were acclimatized to both VP methods. Participants were asked to detect and navigate past five of eight possible obstacles in a white corridor across 20–30 trials. Randomized obstacles included black or white mannequins, black or white overhanging boxes, black or white bins and black or white stationary boxes. The same four participants underwent trials at three different real-word urban locations using both VP methods (randomized order). They were tasked with navigating a complex, dynamic pre-determined scene while detecting, verbally identifying, and avoiding obstacles in their path. Main results. The indoor obstacle course showed that the LBE method (63.6 ± 10.7%, mean ± SD) performed significantly better than L2 (48.5 ± 11.2%), for detection of obstacles (p < 0.001, Mack–Skillings). The real-world assessment showed that of the objects detected, 50.2% (138/275) were correctly identified using LBE and 41.7% (138/331) using L2, corresponding to a risk difference of 8 percentage points, p = 0.081). Significance. Real world outcomes can be improved using an enhanced VP algorithm, providing depth-based visual cues (#NCT05158049).

Objective. A novel method is introduced to regress over the sign language finger movements from human electrocorticography (ECoG) recordings. Approach. The proposed graph-optimized block-term tensor regression (Go-BTTR) method consists of two components: a deflation-based regression model that sequentially Tucker-decomposes multiway ECoG data into a series of blocks, and a causal graph process (CGP) that accounts for the complex relationship between finger movements when expressing sign language gestures. Prior to each regression block, CGP is applied to decide which fingers should be kept separate or grouped and should therefore be referred to BTTR or its extended version eBTTR, respectively. Main results. Two ECoG datasets were used, one recorded in five patients expressing four hand gestures of the American sign language alphabet, and another in two patients expressing all gestures of the Flemish sign language alphabet. As Go-BTTR combines fingers in a flexible way, it can better account for the nonlinear relationship ECoG exhibits when expressing hand gestures, including unintentional finger co-activations. This is reflected by the superior joint finger trajectory predictions compared to eBTTR, and predictions that are on par with BTTR in single-finger scenarios. For the American sign language alphabet (Utrecht dataset), the average correlation across all fingers for all subjects was 0.73 for Go-BTTR, 0.719 for eBTTR and 0.70 for BTTR. For the Flemish sign language alphabet (Leuven dataset), the average correlation across all fingers for all subjects was 0.37 for Go-BTTR, 0.34 for eBTTR and 0.33 for BTTR. Significance. Our findings show that Go-BTTR is capable of decoding complex hand gestures taken from the sign language alphabet. Go-BTTR also demonstrates computational efficiency, providing a notable benefit when intracranial electrodes are inserted during a patient's pre-surgical evaluation. This efficiency helps reduce the time required for developing and testing a brain–computer interface solution.

Objective. With the recent development of visual evoked potential (VEP) based brain–computer interfaces (BCIs), the stimulus paradigm has been continuously innovated, in which the pursuit of higher BCI performance and better user experience has become indispensable. Approach. To optimize the stimulus paradigm, a 12-target online BCI system was designed in this study by adopting flicker for steady-state VEPs, Newton's ring for steady-state motion VEP, and frame rate based video stimulus, respectively. The signal characteristics of VEP, classification accuracy, and user experience of the three stimulus paradigms were quantitatively evaluated and compared. Main results. The online information transfer rates for the three stimulus paradigms were 53.77 bits min⁻¹, 51.41 ± 3.55 bits min⁻¹, and 52.07 ± 3.09 bits min⁻¹, respectively. The video stimulus had a significantly better user experience, while the flicker stimulus showed the worst. Significance. These results demonstrate the advantage of the proposed video stimulus paradigm and have significant theoretical and applied implications for developing VEP-based BCI systems.

Objective. Visual prostheses can provide partial visual function in patients with retinal degenerative diseases. However, in clinical trials, patients implanted with retinal prostheses have reported perceptual fading, which is thought to be related to response desensitization. Additionally, natural stimuli consist of aperiodic events across a short temporal span, whereas periodic stimulation (fixed inter-pulse intervals (IPIs)) is the standard approach in retinal prosthesis research. In this study, we investigated how aperiodic stimulation of the epiretinal surface affects electrically evoked responses in the primary visual cortex (V1) compared with periodic stimulation. Approach. In vivo experiments were conducted in healthy and retinal-degenerated rats. Periodic stimulation consisted of constant IPIs, whereas aperiodic stimulation was provided by mixed IPIs. We calculated the spike time tiling coefficient to assess response consistency across trials, the significant response ratio, and the spike rate to analyze response desensitization. Main results. The results showed a significantly lower consistency of cortical responses in retinal degenerated rats than in healthy rats at 5 Hz. The consistency of the response to periodic stimulation decreased considerably as the frequency was increased to 10 Hz and 20 Hz in both groups and was greatly improved by applying aperiodic stimulation. In addition, aperiodic stimulation evoked a significantly higher spike rate in response to continuous stimulation at high frequencies (e.g. 10 and 20 Hz). Significance. By applying electrical stimulation with varying IPIs directly on the epiretinal surface, we observed promising results in terms of enhancing cortical response consistency and reducing desensitization. This finding presents a potential approach to enhance the effectiveness of retinal prostheses.

Transcranial magnetic resonance-guided focused ultrasound (MRgFUS) represents a transformative modality in treating neurological disorders and diseases, offering precise, minimally invasive interventions for conditions such as essential tremor and Parkinson's disease. Objective. This paper presents an industry-focused perspective on the current state of MRgFUS, highlighting recent advancements, challenges, and emerging opportunities within the field. Approach. We review key clinical applications and therapeutic mechanisms, focusing on targeted ablation, while discussing technological innovations that support new indications. Current regulatory frameworks, challenges in device development, and market trends are examined to provide an understanding of the industry landscape. Main results. We indicate some limitations in MRgFUS and suggest potential strategies for overcoming these limitations to optimize treatment outcomes. Significance. We conclude with an outlook on promising developments, including artificial intelligence-enhanced targeting, low and high-field magnetic resonance imaging integration, and multimodal imaging techniques, that could potentially drive further innovation and adoption of MRgFUS in brain therapy.

Objective. Machine learning's (MLs) ability to capture intricate patterns makes it vital in neural engineering research. With its increasing use, ensuring the validity and reproducibility of ML methods is critical. Unfortunately, this has not always been the case in practice, as there have been recent retractions across various scientific fields due to the misuse of ML methods and validation procedures. To address these concerns, we propose the first version of the neural engineering reproducibility and validity essentials for ML (NERVE-ML) checklist, a framework designed to promote the transparent, reproducible, and valid application of ML in neural engineering. Approach. We highlight some of the unique challenges of model validation in neural engineering, including the difficulties from limited subject numbers, repeated or non-independent samples, and high subject heterogeneity. Through detailed case studies, we demonstrate how different validation approaches can lead to divergent scientific conclusions, highlighting the importance of selecting appropriate procedures guided by the NERVE-ML checklist. Effectively addressing these challenges and properly scoping scientific conclusions will ensure that ML contributes to, rather than hinders, progress in neural engineering. Main results. Our case studies demonstrate that improper validation approaches can result in flawed studies or overclaimed scientific conclusions, complicating the scientific discourse. The NERVE-ML checklist effectively addresses these concerns by providing guidelines to ensure that ML approaches in neural engineering are reproducible and lead to valid scientific conclusions. Significance. By effectively addressing these challenges and properly scoping scientific conclusions guided by the NERVE-ML checklist, we aim to help pave the way for a future where ML reliably enhances the quality and impact of neural engineering research.

Objective. Sharing computational models offers many benefits, including increased scientific rigor during project execution, readership of the associated paper, resource usage efficiency, replicability, and reusability. In recognition of the growing practice and requirement of sharing models, code, and data, herein, we provide guidance to facilitate sharing of computational models by providing an accessible resource for regular reference throughout a project's stages. Approach. We synthesized literature on good practices in scientific computing and on code and data sharing with our experience in developing, sharing, and using models of neural stimulation, although the guidance will also apply well to most other types of computational models. Main results. We first describe the '6 R' characteristics of shared models, leaning on prior scientific computing literature, which enforce accountability and enable advancement: re-runnability, repeatability, replicability, reproducibility, reusability, and readability. We then summarize action items associated with good practices in scientific computing, including selection of computational tools during project planning, code and documentation design during development, and user instructions for deployment. We provide a detailed checklist of the contents of shared models and associated materials, including the model itself, code for reproducing published figures, documentation, and supporting datasets. We describe code, model, and data repositories, including a list of characteristics to consider when selecting a platform for sharing. We describe intellectual property (IP) considerations to balance permissive, open-source licenses versus software patents and bespoke licenses that govern and incentivize commercialization. Finally, we exemplify these practices with our ASCENT pipeline for modeling peripheral nerve stimulation. Significance. We hope that this paper will serve as an important and actionable reference for scientists who develop models—from project planning through publication—as well as for model users, institutions, IP experts, journals, funding sources, and repository platform developers.

The Tenth International brain–computer interface (BCI) meeting was held June 6–9, 2023, in the Sonian Forest in Brussels, Belgium. At that meeting, 21 master classes, organized by the BCI Society's Postdoc & Student Committee, supported the Society's goal of fostering learning opportunities and meaningful interactions for trainees in BCI-related fields. Master classes provide an informal environment where senior researchers can give constructive feedback to the trainee on their chosen and specific pursuit. The topics of the master classes span the whole gamut of BCI research and techniques. These include data acquisition, neural decoding and analysis, invasive and noninvasive stimulation, and ethical and transitional considerations. Additionally, master classes spotlight innovations in BCI research. Herein, we discuss what was presented within the master classes by highlighting each trainee and expert researcher, providing relevant background information and results from each presentation, and summarizing discussion and references for further study.

The brain is a highly complex physical system made of assemblies of neurons that work together to accomplish elaborate tasks such as motor control, memory and perception. How these parts work together has been studied for decades by neuroscientists using neuroimaging, psychological manipulations, and neurostimulation. Neurostimulation has gained particular interest, given the possibility to perturb the brain and elicit a specific response. This response depends on different parameters such as the intensity, the location and the timing of the stimulation. However, most of the studies performed so far used previously established protocols without considering the ongoing brain activity and, thus, without adaptively targeting the stimulation. In control theory, this approach is called open-loop control, and it is always paired with a different form of control called closed-loop, in which the current activity of the brain is used to establish the next stimulation. Recently, neuroscientists are beginning to shift from classical fixed neuromodulation studies to closed-loop experiments. This new approach allows the control of brain activity based on responses to stimulation and thus to personalize individual treatment in clinical conditions. Here, we review this new approach by introducing control theory and focusing on how these aspects are applied in brain studies. We also present the different stimulation techniques and the control approaches used to steer the brain. Finally, we explore how the closed-loop framework will revolutionize the way the human brain can be studied, including a discussion on open questions and an outlook on future advances.

Objective. Intracellular patch-clamp electrophysiology, one of the most ubiquitous, high-fidelity techniques in biophysics, remains laborious and low-throughput. While previous efforts have succeeded at automating some steps of the technique, here we demonstrate a robotic 'PatcherBot' system that can perform many patch-clamp recordings sequentially, fully unattended. Approach. Comprehensive automation is accomplished by outfitting the robot with machine vision, and cleaning pipettes instead of manually exchanging them. Main results. the PatcherBot can obtain data at a rate of 16 cells per hour and work with no human intervention for up to 3 h. We demonstrate the broad applicability and scalability of this system by performing hundreds of recordings in tissue culture cells and mouse brain slices with no human supervision. Using the PatcherBot, we also discovered that pipette cleaning can be improved by a factor of three. Significance. The system is potentially transformative for applications that depend on many high-quality measurements of single cells, such as drug screening, protein functional characterization, and multimodal cell type investigations.

Objective. Most brain–computer interfaces (BCIs) based on functional near-infrared spectroscopy (fNIRS) require that users perform mental tasks such as motor imagery, mental arithmetic, or music imagery to convey a message or to answer simple yes or no questions. These cognitive tasks usually have no direct association with the communicative intent, which makes them difficult for users to perform. Approach. In this paper, a 3-class intuitive BCI is presented which enables users to directly answer yes or no questions by covertly rehearsing the word 'yes' or 'no' for 15 s. The BCI also admits an equivalent duration of unconstrained rest which constitutes the third discernable task. Twelve participants each completed one offline block and six online blocks over the course of two sessions. The mean value of the change in oxygenated hemoglobin concentration during a trial was calculated for each channel and used to train a regularized linear discriminant analysis (RLDA) classifier. Main results. By the final online block, nine out of 12 participants were performing above chance (p  <  0.001 using the binomial cumulative distribution), with a 3-class accuracy of 83.8%  ±  9.4%. Even when considering all participants, the average online 3-class accuracy over the last three blocks was 64.1 %  ±  20.6%, with only three participants scoring below chance (p  <  0.001). For most participants, channels in the left temporal and temporoparietal cortex provided the most discriminative information. Significance. To our knowledge, this is the first report of an online 3-class imagined speech BCI. Our findings suggest that imagined speech can be used as a reliable activation task for selected users for development of more intuitive BCIs for communication.

Objective. Optical techniques for recording and manipulating neural activity have traditionally been constrained to superficial brain regions due to light scattering. New techniques are needed to extend optical access to large 3D volumes in deep brain areas, while retaining local connectivity. Approach. We have developed a method to implant bundles of hundreds or thousands of optical microfibers, each with a diameter of 8 μm. During insertion, each fiber moves independently, following a path of least resistance. The fibers achieve near total internal reflection, enabling optically interfacing with the tissue near each fiber aperture. Main results. At a depth of 3 mm, histology shows fibers consistently splay over 1 mm in diameter throughout the target region. Immunohistochemical staining after chronic implants reveals neurons in close proximity to the fiber tips. Models of photon fluence indicate that fibers can be used as a stimulation light source to precisely activate distinct patterns of neurons by illuminating a subset of fibers in the bundle. By recording fluorescent beads diffusing in water, we demonstrate the recording capability of the fibers. Significance. Our histology, modeling and fluorescent bead recordings suggest that the optical microfibers may provide a minimally invasive, stable, bidirectional interface for recording or stimulating genetic probes in deep brain regions—a hyper-localized form of fiber photometry.

Objective. Stereotactic frame systems are the gold-standard for stereotactic surgeries, such as implantation of deep brain stimulation (DBS) devices for treatment of medically resistant neurologic and psychiatric disorders. However, frame-based systems require that the patient is awake with a stereotactic frame affixed to their head for the duration of the surgical planning and implantation of the DBS electrodes. While frameless systems are increasingly available, a reusable re-attachable frame system provides unique benefits. As such, we created a novel reusable MRI-compatible stereotactic frame system that maintains clinical accuracy through the detachment and reattachment of its stereotactic devices used for MRI-guided neuronavigation. Approach. We designed a reusable arc-centered frame system that includes MRI-compatible anchoring skull screws for detachment and re-attachment of its stereotactic devices. We validated the stability and accuracy of our system through phantom, in vivo mock-human porcine DBS-model and human cadaver testing. Main results. Phantom testing achieved a root mean square error (RMSE) of 0.94  ±  0.23 mm between the ground truth and the frame-targeted coordinates; and achieved an RMSE of 1.11  ±  0.40 mm and 1.33  ±  0.38 mm between the ground truth and the CT- and MRI-targeted coordinates, respectively. In vivo and cadaver testing achieved a combined 3D Euclidean localization error of 1.85  ±  0.36 mm (p  <  0.03) between the pre-operative MRI-guided placement and the post-operative CT-guided confirmation of the DBS electrode. Significance. Our system demonstrated consistent clinical accuracy that is comparable to conventional frame and frameless stereotactic systems. Our frame system is the first to demonstrate accurate relocation of stereotactic frame devices during in vivo MRI-guided DBS surgical procedures. As such, this reusable and re-attachable MRI-compatible system is expected to enable more complex, chronic neuromodulation experiments, and lead to a clinically available re-attachable frame that is expected to decrease patient discomfort and costs of DBS surgery.

Objective. Our purpose was to evaluate the ability of a dual electrode approach to remove motion artifact from electroencephalography (EEG) measurements. Approach. We used a phantom human head model and robotic motion platform to induce motion while collecting scalp EEG. We assembled a dual electrode array capturing (a) artificial neural signals plus noise from scalp EEG electrodes, and (b) electrically isolated motion artifact noise. We recorded artificial neural signals broadcast from antennae in the phantom head during continuous vertical sinusoidal movements (stationary, 1.00, 1.25, 1.50, 1.75, 2.00 Hz movement frequencies). We evaluated signal quality using signal-to-noise ratio (SNR), cross-correlation, and root mean square error (RMSE) between the ground truth broadcast signals and the recovered EEG signals. Main results. Signal quality was restored following noise cancellation when compared to single electrode EEG measurements collected with no phantom head motion. Significance. We achieved substantial motion artifact attenuation using secondary electrodes for noise cancellation. These methods can be applied to studying electrocortical signals during human locomotion to improve real-world neuroimaging using EEG.

Objective: Extracting universal, task-independent semantic features from electroencephalography (EEG) signals remains an open challenge. Traditional approaches are often task-specific, limiting their generalization across different EEG paradigms. This study aims to develop a robust, unsupervised framework for learning high-level, task-independent neural representations.&#xD;Approach: We propose a novel framework integrating convolutional neural networks (CNNs), AutoEncoders, and Transformers to extract both low-level spatiotemporal patterns and high-level semantic features from EEG signals. The model is trained in an unsupervised manner to ensure adaptability across diverse EEG paradigms, including motor imagery (MI), steady-state visually evoked potentials&#xD;(SSVEP), and event-related potentials (ERP, specifically P300). Extensive analyses, including clustering, correlation, and ablation studies, are conducted to validate the quality and interpretability of the extracted features.&#xD;Main Results: Our method achieves state-of-the-art performance, with average classification accuracies of 83.50% and 84.84% on MI datasets (BCICIV 2a and BCICIV 2b), 98.41% and 99.66% on SSVEP datasets (Lee2019-SSVEP and Nakanishi2015), and an average AUC of 91.80% across eight ERP datasets. t-SNE and clustering analyses reveal that the extracted features exhibit enhanced separability and structure compared to raw EEG data. Correlation studies confirm the framework's ability to balance universal and subject-specific features, while ablation results highlight the near-optimality of the&#xD;selected model configuration.&#xD;Significance: This work establishes a universal framework for task-independent semantic feature extraction from EEG signals, bridging the gap between conventional feature engineering and modern deep learning (DL) methods. By providing robust, generalizable representations across diverse EEG paradigms, this approach lays the foundation for advanced brain-computer interface (BCI) appli-&#xD;cations, cross-task EEG analysis, and future developments in semantic EEG processing.

Objective&#xD;Our objective was to perform a complete analysis of in-vitro impedance data for sputtered iridium oxide film (SIROF) micro-electrodes. The analysis included quantification of the stochastic and bias error structure and development of a process model that accounted for the chemistry and physics of the electrode-electrolyte interface.&#xD;Approach&#xD;The measurement model program was used to analyze electrochemical impedance spectroscopy (EIS) data for sputtered iridium oxide film (SIROF) micro-electrodes at potentials ranging from −0.4 to +0.6 V(Ag|AgCl). The frequency range used for the analysis was that determined to be consistent with the Kramers–Kronig relations. Interpretation of the data was enabled by truncating frequencies at which the ohmic impedance influenced the impedance.&#xD;Main Results&#xD;An interpretation model was developed that considered the impedance of the bare surface and the contribution of a porous component, based on the de Levie model of porous electrodes. The influence of iridium oxidation state on impedance was included. The proposed model fit all 36 EIS spectra well. The effective capacitance of the SIROF system ranged from 32 mF/cm2 at −0.4 V(Ag|AgCl) to a maximum of 93 mF/cm2 at 0.2 and 0.4 V(Ag|AgCl).&#xD;Significance&#xD;The model developed to interpret the impedance response of neural stimulation electrodes in vitro guides model development for in-vivo studies.

Objective. Cochlear implants are among the few clinical interventions for people with severe or profound hearing loss. However, current spread during monopolar electrical stimulation results in poor spectral resolution, prompting the exploration of optical stimulation as an alternative approach. Enabled by introducing light-sensitive ion channels into auditory neurons (optogenetics), optical stimulation has been shown to activate a more discrete neural area with minimal overlap between each frequency channel during simultaneous stimulation. However, the utility of optogenetic approaches is uncertain due to the low fidelity of responses to light and high-power requirements compared to electrical stimulation. Approach. Hybrid stimulation, combining sub-threshold electrical and optical pulses, has been shown to improve fidelity and use less light, but the impact on spread of activation and channel summation using a translatable, multi-channel hybrid implant is unknown. This study examined these factors during single channel and simultaneous multi-channel hybrid stimulation in transgenic mice expressing the ChR2/H134R opsin. Acutely deafened mice were implanted with a hybrid cochlear array containing alternating light emitting diodes and platinum electrode rings. Spiking activity in the inferior colliculus was recorded during electrical-only or hybrid stimulation in which optical and electrical stimuli were both at sub-threshold intensities. Thresholds, spread of activation, and threshold shifts during simultaneous hybrid stimulation were compared to electrical-only stimulation. Main results. The electrical current required to reach activation threshold during hybrid stimulation was reduced by 7.3 dB compared to electrical-only stimulation (p<0.001). The activation width measured at two levels of discrimination above threshold and channel summation during simultaneous hybrid stimulation were significantly lower compared to electrical-only stimulation (p<0.05), but there was no spatial advantage of hybrid stimulation at higher electrical stimulation levels. Significance. Reduced channel interaction would facilitate multi-channel simultaneous stimulation, thereby enhancing the perception of temporal fine structure which is crucial for music and speech in noise.

Objective: The study of neurovascular coupling (NVC), the relationship between neuronal activity and cerebral blood flow, is essential for understanding brain physiology in both healthy and pathological states. Current methods to study NVC include neuroimaging techniques with limited temporal resolution and indirect neuronal activity measures. Methods including electroencephalographic (EEG) data are predominantly linear and display limitations that nonlinear methods address. Transfer Entropy (TE) explores linear and nonlinear relationships simultaneously. This study hypothesizes that complex NVC interactions in stroke patients, both linear and&#xD;nonlinear, can be detected using TE. Approach: TE between simultaneously recorded EEG and cerebral blood flow velocity (CBFV) signals was computed and analyzed in three settings: ipsilateral (EEG and CBFV from same hemisphere) stroke and nonstroke, and contralateral (EEG from stroke hemisphere, CBFV from nonstroke hemisphere). A surrogate analysis was performed to evaluate the significance of TE values and to identify the nature of the interactions. Main results: The results showed that EEG generally influenced CBFV. There were more linear+nonlinear interactions in the ipsilateral nonstroke setting and in the delta band in ipsilateral&#xD;stroke and contralateral settings. Interactions between EEG and CBFV were stronger on the nonstroke side for linear+nonlinear dynamics. The strength and nature of the interactions were weakly correlated with clinical outcomes (e.g., delta band (p<0.05): infarct growth linear = -0.448, linear+nonlinear = -0.339; NHISS linear = -0.473, linear+nonlinear = -0.457). Significance: This study exemplifies the benefits of using TE in linear and nonlinear NVC analysis to better understand the implications of these dynamics in stroke severity.

N/A

Objective: Extracting universal, task-independent semantic features from electroencephalography (EEG) signals remains an open challenge. Traditional approaches are often task-specific, limiting their generalization across different EEG paradigms. This study aims to develop a robust, unsupervised framework for learning high-level, task-independent neural representations.&#xD;Approach: We propose a novel framework integrating convolutional neural networks (CNNs), AutoEncoders, and Transformers to extract both low-level spatiotemporal patterns and high-level semantic features from EEG signals. The model is trained in an unsupervised manner to ensure adaptability across diverse EEG paradigms, including motor imagery (MI), steady-state visually evoked potentials&#xD;(SSVEP), and event-related potentials (ERP, specifically P300). Extensive analyses, including clustering, correlation, and ablation studies, are conducted to validate the quality and interpretability of the extracted features.&#xD;Main Results: Our method achieves state-of-the-art performance, with average classification accuracies of 83.50% and 84.84% on MI datasets (BCICIV 2a and BCICIV 2b), 98.41% and 99.66% on SSVEP datasets (Lee2019-SSVEP and Nakanishi2015), and an average AUC of 91.80% across eight ERP datasets. t-SNE and clustering analyses reveal that the extracted features exhibit enhanced separability and structure compared to raw EEG data. Correlation studies confirm the framework's ability to balance universal and subject-specific features, while ablation results highlight the near-optimality of the&#xD;selected model configuration.&#xD;Significance: This work establishes a universal framework for task-independent semantic feature extraction from EEG signals, bridging the gap between conventional feature engineering and modern deep learning (DL) methods. By providing robust, generalizable representations across diverse EEG paradigms, this approach lays the foundation for advanced brain-computer interface (BCI) appli-&#xD;cations, cross-task EEG analysis, and future developments in semantic EEG processing.

Objective&#xD;Our objective was to perform a complete analysis of in-vitro impedance data for sputtered iridium oxide film (SIROF) micro-electrodes. The analysis included quantification of the stochastic and bias error structure and development of a process model that accounted for the chemistry and physics of the electrode-electrolyte interface.&#xD;Approach&#xD;The measurement model program was used to analyze electrochemical impedance spectroscopy (EIS) data for sputtered iridium oxide film (SIROF) micro-electrodes at potentials ranging from −0.4 to +0.6 V(Ag|AgCl). The frequency range used for the analysis was that determined to be consistent with the Kramers–Kronig relations. Interpretation of the data was enabled by truncating frequencies at which the ohmic impedance influenced the impedance.&#xD;Main Results&#xD;An interpretation model was developed that considered the impedance of the bare surface and the contribution of a porous component, based on the de Levie model of porous electrodes. The influence of iridium oxidation state on impedance was included. The proposed model fit all 36 EIS spectra well. The effective capacitance of the SIROF system ranged from 32 mF/cm2 at −0.4 V(Ag|AgCl) to a maximum of 93 mF/cm2 at 0.2 and 0.4 V(Ag|AgCl).&#xD;Significance&#xD;The model developed to interpret the impedance response of neural stimulation electrodes in vitro guides model development for in-vivo studies.

Objective. Cochlear implants are among the few clinical interventions for people with severe or profound hearing loss. However, current spread during monopolar electrical stimulation results in poor spectral resolution, prompting the exploration of optical stimulation as an alternative approach. Enabled by introducing light-sensitive ion channels into auditory neurons (optogenetics), optical stimulation has been shown to activate a more discrete neural area with minimal overlap between each frequency channel during simultaneous stimulation. However, the utility of optogenetic approaches is uncertain due to the low fidelity of responses to light and high-power requirements compared to electrical stimulation. Approach. Hybrid stimulation, combining sub-threshold electrical and optical pulses, has been shown to improve fidelity and use less light, but the impact on spread of activation and channel summation using a translatable, multi-channel hybrid implant is unknown. This study examined these factors during single channel and simultaneous multi-channel hybrid stimulation in transgenic mice expressing the ChR2/H134R opsin. Acutely deafened mice were implanted with a hybrid cochlear array containing alternating light emitting diodes and platinum electrode rings. Spiking activity in the inferior colliculus was recorded during electrical-only or hybrid stimulation in which optical and electrical stimuli were both at sub-threshold intensities. Thresholds, spread of activation, and threshold shifts during simultaneous hybrid stimulation were compared to electrical-only stimulation. Main results. The electrical current required to reach activation threshold during hybrid stimulation was reduced by 7.3 dB compared to electrical-only stimulation (p<0.001). The activation width measured at two levels of discrimination above threshold and channel summation during simultaneous hybrid stimulation were significantly lower compared to electrical-only stimulation (p<0.05), but there was no spatial advantage of hybrid stimulation at higher electrical stimulation levels. Significance. Reduced channel interaction would facilitate multi-channel simultaneous stimulation, thereby enhancing the perception of temporal fine structure which is crucial for music and speech in noise.

Objective. Visual prostheses can provide partial visual function in patients with retinal degenerative diseases. However, in clinical trials, patients implanted with retinal prostheses have reported perceptual fading, which is thought to be related to response desensitization. Additionally, natural stimuli consist of aperiodic events across a short temporal span, whereas periodic stimulation (fixed inter-pulse intervals (IPIs)) is the standard approach in retinal prosthesis research. In this study, we investigated how aperiodic stimulation of the epiretinal surface affects electrically evoked responses in the primary visual cortex (V1) compared with periodic stimulation. Approach. In vivo experiments were conducted in healthy and retinal-degenerated rats. Periodic stimulation consisted of constant IPIs, whereas aperiodic stimulation was provided by mixed IPIs. We calculated the spike time tiling coefficient to assess response consistency across trials, the significant response ratio, and the spike rate to analyze response desensitization. Main results. The results showed a significantly lower consistency of cortical responses in retinal degenerated rats than in healthy rats at 5 Hz. The consistency of the response to periodic stimulation decreased considerably as the frequency was increased to 10 Hz and 20 Hz in both groups and was greatly improved by applying aperiodic stimulation. In addition, aperiodic stimulation evoked a significantly higher spike rate in response to continuous stimulation at high frequencies (e.g. 10 and 20 Hz). Significance. By applying electrical stimulation with varying IPIs directly on the epiretinal surface, we observed promising results in terms of enhancing cortical response consistency and reducing desensitization. This finding presents a potential approach to enhance the effectiveness of retinal prostheses.

Objective. Non-invasive brain stimulation (NIBS) offers therapeutic benefits for various brain disorders. Personalization may enhance these benefits by optimizing stimulation parameters for individual subjects. Approach. We present a computational pipeline for simulating and assessing the effects of NIBS using personalized, large-scale brain network activity models. Using structural MRI and diffusion-weighted imaging data, the pipeline leverages a convolutional neural network-based segmentation algorithm to generate subject-specific head models with up to 40 tissue types and personalized dielectric properties. We integrate electromagnetic simulations of NIBS exposure with whole-brain network models to predict NIBS-dependent perturbations in brain dynamics, simulate the resulting EEG traces, and quantify metrics of brain dynamics. Main results. The pipeline is implemented on o²S²PARC, an open, cloud-based infrastructure designed for collaborative and reproducible computational life science. Furthermore, a dedicated planning tool provides guidance for optimizing electrode placements for transcranial temporal interference stimulation. In two proof-of-concept applications, we demonstrate that: (i) transcranial alternating current stimulation produces expected shifts in the EEG spectral response, and (ii) simulated baseline network activity exhibits physiologically plausible fluctuations in inter-hemispheric synchronization. Significance. This pipeline facilitates a shift from exposure-based to response-driven optimization of NIBS, supporting new stimulation paradigms that steer brain dynamics towards desired activity patterns in a controlled manner.

Objective: The study of neurovascular coupling (NVC), the relationship between neuronal activity and cerebral blood flow, is essential for understanding brain physiology in both healthy and pathological states. Current methods to study NVC include neuroimaging techniques with limited temporal resolution and indirect neuronal activity measures. Methods including electroencephalographic (EEG) data are predominantly linear and display limitations that nonlinear methods address. Transfer Entropy (TE) explores linear and nonlinear relationships simultaneously. This study hypothesizes that complex NVC interactions in stroke patients, both linear and&#xD;nonlinear, can be detected using TE. Approach: TE between simultaneously recorded EEG and cerebral blood flow velocity (CBFV) signals was computed and analyzed in three settings: ipsilateral (EEG and CBFV from same hemisphere) stroke and nonstroke, and contralateral (EEG from stroke hemisphere, CBFV from nonstroke hemisphere). A surrogate analysis was performed to evaluate the significance of TE values and to identify the nature of the interactions. Main results: The results showed that EEG generally influenced CBFV. There were more linear+nonlinear interactions in the ipsilateral nonstroke setting and in the delta band in ipsilateral&#xD;stroke and contralateral settings. Interactions between EEG and CBFV were stronger on the nonstroke side for linear+nonlinear dynamics. The strength and nature of the interactions were weakly correlated with clinical outcomes (e.g., delta band (p<0.05): infarct growth linear = -0.448, linear+nonlinear = -0.339; NHISS linear = -0.473, linear+nonlinear = -0.457). Significance: This study exemplifies the benefits of using TE in linear and nonlinear NVC analysis to better understand the implications of these dynamics in stroke severity.

Objective. The identification of individual neuronal activity from multielectrode arrays poses significant challenges, including handling data from numerous electrodes, resolving overlapping action potentials and tracking activity across long recordings. This study introduces NeuroNella, an automated algorithm developed to address these challenges. Approach. NeuroNella employs blind source separation to leverage the sparsity of action potentials in multichannel recordings. It was validated using three datasets, including two publicly available ones: (1) in vitro recordings (252 channels) of retinal ganglion cells from mice with simultaneous ground-truth loose patch data to assess accuracy; (2) a Neuropixel recording from an awake mouse, comprising 374 channels spanning different brain areas, to demonstrate scalability with dense multielectrode configurations in in vivo recordings; and (3) data (32 channels) recorded from the medullary reticular formation in a terminally anaesthetised macaque, to showcase decomposition over long periods of time. Main results. The algorithm exhibited an error rate of less than 1% compared to ground-truth data. It reliably identified individual neurons, detected neuronal activity across a wide amplitude range, and tolerated minor probe shifts, maintaining robustness in prolonged experimental sessions. Significance. NeuroNella provides an automated and efficient method for neuronal activity identification. Its adaptability to diverse dataset, species, and recording configurations underscores its potential to advance studies of neuronal dynamics and facilitate real-time neuronal decoding systems.

Objective. Muscle fiber conduction velocity (MFCV) describes the speed at which electrical activity propagates along muscle fibers and is typically assessed using invasive or surface electromyography. Because electrical currents generate magnetic fields, propagation velocity can potentially also be measured magnetically using magnetomyography (MMG), offering the advantage of a contactless approach. Approach. To test this hypothesis, we recorded MMG signals from the right biceps brachii muscle of 24 healthy subjects (12 male, 12 female) using a linear array of seven optically pumped magnetometers (OPMs). Subjects maintained muscle force for 30 s at 20%, 40%, and 60% of their maximum voluntary contraction. Main results. In 20 subjects, propagation of MMG signals was observable. Change in polarity and signal cancellation enabled localization of the innervation zone. We estimated the MFCV for each condition by cross-correlating double-differentiated MMG signals. To validate our results, we examined whether MFCV estimations increased with higher force levels, a well-documented characteristic of the neuromuscular system. The median MFCV significantly increased with force (p = 0.007), with median values of 3.2 m s ⁻¹ at 20%, 3.8 m s ⁻¹ at 40%, and 4.4 m s ⁻¹ at 60% across all 20 subjects. Significance. Our results establish the first measurements of magnetic MFCV in MMG using OPMs. These findings pave the way for further developments and application of quantum sensors for contactless clinical neurophysiology.

Objective. Modern neural devices allow to interact with degenerated tissue in order to restore sensoric loss function and to suppress symptoms of neurodegenerative diseases using microelectronic arrays (MEA). They have a bidirectional interface for performing electrical stimulation to write-in new information and for recording the neural activity to read-out a neural task, e.g. movement ambitions. For both applications, the electrical impedance of the electrode-tissue interface (ETI) is crucial. However, the ETI can change during run-time due to encapsulation effects and changes of the neuronal structures. We investigated if an impedance spectrum can be reliably extracted from recordings during stimulation with microelectrode arrays. Approach. We present a measurement method for characterizing the electrical impedance spectrum during stimulation. We performed charge-controlled stimulation with a penetrating microelectrode array in an electrolyte solution. From the stimulation recordings, we extracted the impedance. Furthermore, a numerical model (digital twin) of the stimulation electrodes is established. Main results. We obtained consistent results for relevant electrochemical using electrochemical impedance spectroscopy, time-domain analysis and Fourier-transform-based impedance estimation. Moreover, the numerical simulations confirmed that the measured microelectrode had the expected properties. Significance. Our results pave the way to enable a live assessment of the impedance in future MEA-based neural devices. This will enable adaptive electrical stimulation or (re-)selection of recording electrodes by taking the actual state of the electrode into account.

Objective. Resting electroencephalographic activity is typically indistinguishable from a filtered linear random process across a diverse range of behavioural and pharmacological states, suggesting that the power spectral density of the resting electroencephalogram (EEG) can be modelled as the superposition of multiple, stochastically driven and independent, alpha band (8–13 Hz) relaxation oscillators. This simple model can account for variations in alpha band power and '1/f scaling' in eyes-open/eyes-closed conditions in terms of alterations in the distribution of the alpha band oscillatory relaxation rates. As changes in alpha band power and '1/f scaling' have been reported in anaesthesia we hypothesise that such changes may also be accounted for by alterations in alpha band relaxation oscillatory rate distributions. Approach. On this basis we choose to study the EEG activity of xenon and nitrous oxide, gaseous anaesthetic agents that have been reported to produce different EEG effects, notable given they are both regarded as principally acting via N-methyl-D-aspartate (NMDA) receptor antagonism. By recording high density EEG from participants receiving equilibrated step-level increases in inhaled concentrations of xenon (n = 24) and nitrous oxide (n = 20), alpha band relaxation rate (damping) distributions were estimated by solving an inhomogeneous integral equation describing the linear superposition of multiple alpha-band relaxation oscillators having a continuous distribution of dampings. Main results. For both agents, level-dependent reductions in alpha band power and spectral slope exponent (15–40 Hz) were observed, that were accountable by increases in mean alpha band damping. Significance. These shared increases suggest that, consistent with their identified molecular targets of action, xenon and nitrous oxide are mechanistically similar, a conclusion further supported by neuronal population modelling in which NMDA antagonism is associated with increases in damping and reductions in peak alpha frequency. Alpha band damping may provide an important link between experiment and theories of consciousness, such as the global neuronal network theory, where the likelihood of a globally excited state ('conscious percept'), is inversely related to mean damping.