Journal of Neural Engineering

ObjectiveBiopotential measurement devices, such as EEG, ECG, and EMG recorders, are available in low-cost, open-source implementations with standard specifications. However, high-end systems remain expensive and predominantly proprietary, limiting accessibility and customization by research laboratories. In addition, neurophysiology techniques such as bioimpedance-based Fast Neural Electrical Impedance Tomography (FN-EIT) also rely on these systems for data acquisition. This work aimed to develop an open-source biopotential recording system using off-the-shelf components that achieves performance comparable to high-end devices. ApproachWe designed our system to provide simultaneous sampling over 32 channels, 24-bit resolution, 10 kHz bandwidth, 50 kHz sampling rate, and battery-powered operation while reducing cost by two orders of magnitude. System performance was evaluated comparatively against a reference device. Initial validation involved benchtop recordings in saline solution and standard non-invasive biopotential measurements (ECG and EMG). Further in-vivo validation was performed by recording evoked electrophysiological responses and FN-EIT traces from the sciatic nerve of a rat during tibial branch stimulation. Main resultsEMG recordings showed comparable RMS peak amplitudes (814{+/-}153 {micro}V vs. 897{+/-}113{micro}V, p=0.07), while ECG-derived heart rates closely matched between systems (64.8{+/-}4.0 bpm vs. 65.1{+/-}3.1bpm, p=0.54). During in-vivo recordings, compound action potentials exhibited comparable amplitudes and morphology (129{+/-}26 mV vs 128{+/-}25 mV, P=0.15). FN-EIT recordings showed strongly correlated baseline voltages (R>0.93, P=0.11), sub-microvolt noise levels (0.83{+/-}0.36{micro}V vs. 0.42{+/-}0.25{micro}V, p<0.05), and comparable impedance variations (0.006{+/-}0.002% vs 0.005{+/-}0.003, P>0.05). FN-EIT images of functional activity recorded with the novel device closely matched reference ones, exhibiting a 98.5% overlap in activated area. SignificanceThe proposed open-source device has the potential to broaden research access to customizable, high-specification data acquisition hardware and facilitate wider adoption of specialized neural recording techniques such as FN-EIT.

Extracellular sinusoidal low frequency alternating current (LFAC) stimulation of peripheral motor nerves has been observed to induce size wise activation of nerve fibers, unlike the inverse recruitment order typically seen in extracellular pulsed stimulation. This study aims to explore potential biophysical mechanisms responsible for this phenomenon using computational modeling. Volume conductor model was utilized with a bipolar cuff electrode encasing a single rat-sized fascicle. The extracellular potentials generated by LFAC and pulse stimulation were projected onto the McIntyre-Richardson-Grill models of myelinated motor nerve fibers to examine the activation of fibers ranging from 5.7 to 16m in diameter. Intracellular and extracellular stimulation were compared for strength-frequency relationships with LFAC (1-20Hz) and strength-duration curves for pulse stimulation. The threshold tracking technique was used to study membrane electrotonus and threshold electrotonus of different fibers to examine subthreshold accommodation in response to LFAC and prolonged pulse stimulation. The simulations revealed that the inverse order of fiber recruitment is an inherent characteristic of extracellular stimulation and is theoretically independent of the stimulation waveform. LFAC showed an inverse strength-frequency relationship (higher frequency, lower threshold current), similar to the inverse strength-duration relationship for pulsed stimulation. Analysis of subthreshold accommodation showed that larger fibers exhibit greater accommodation than smaller fibers, leading to increased activation thresholds as fast Na+ activation factor m3h decreases while slow K+ activation increases, supporting accommodation as a contributor to orderly recruitment. With increasing LFAC frequency (up to 20Hz), these accommodation characteristics were reduced and large-fiber state dynamics shifted toward those of smaller fibers. LFAC was also found to induce subthreshold oscillations that promoted spike initiation during slow depolarization. These findings suggest that LFAC provides a controlled and optimized method for achieving orderly recruitment without the need for complex selective blocking protocols. By leveraging intrinsic membrane properties, LFAC offers a neuromodulation strategy that preserves physiological recruitment order, with direct implications for selective nerve stimulation in clinical and neuroprosthetic applications. Author summaryElectrical stimulation is widely used to activate peripheral nerves in motor rehabilitation and neuroprosthetic devices, but conventional pulse stimulation activates larger nerve fibers first (with lower current intensity), which can induce rapid muscle fatigue and pain. We used well-established and validated computational models of motor nerve fibers (axons) to explore how sinusoidal low frequency alternating current (LFAC) stimulation can produce a more physiological, size-wise recruitment order. We simulated myelinated motor nerve fibers of different diameters individually inside a bipolar cuff electrode and analyzed how the membrane and ion channels changed during stimulation levels that are below thresholds for action potential firing. We found that larger fibers adapt (accommodate) more strongly during the slow depolarization of LFAC: their sodium channels become less open, while potassium activation increases, raising the current required to induce an action potential. Smaller fibers were less affected by this accommodation effect and could reach firing at lower current intensities. We also found that these effects depend on stimulation frequency; at lower frequencies, the accommodation characteristics were more defined (for all fibers), while at higher frequencies they were reduced (for large fibers) and all fiber responses became more similar. Our results suggest that the responses of intrinsic membrane dynamics to LFAC lead fiber recruitment toward a more physiological order, which facilitates the design of safer and more selective nerve stimulation strategies with LFAC.

Objective: Here, we directly demonstrate that extraocular electrical stimulation can reliably activate retinal ganglion cells (RGCs) in vivo and systematically identify optimal stimulation waveforms that maximize RGC activation at tolerable amplitudes. Approach: Using transpupillary two-photon calcium imaging in Thy1-GCaMP6f rats, we directly visualized RGC activation during extraocular electrical stimulation with single-cell resolution. We tested symmetric (SCB 1:1) and asymmetric (ACB 1:4) charge-balanced waveforms across frequencies ranging from 20 to 5,000 Hz and amplitudes from 1 to 300 uA, correlating cellular calcium responses with behavioral outcomes during awake stimulation. Main Results: ACB 1:4 stimulation at lower frequencies (20-50 Hz) robustly and reliably activates RGC somas in vivo, producing larger calcium responses at lower amplitudes than SCB 1:1. At comparable amplitudes, ACB stimulation generated 1.8-fold greater calcium responses. In contrast, SCB stimulation required higher amplitudes that exceeded animal tolerance before reliable RGC activation could be achieved. Significance: These findings provide direct evidence that extraocular electrodes can reliably activate RGCs in vivo. Notably, stimulation parameters previously associated with full-length optic nerve regeneration were found to be minimally effective at activating RGCs in vivo, suggesting increased gains could be had with newer approaches. The results further demonstrate that waveform asymmetry improves the efficiency of optic nerve stimulation by engaging RGC somas at lower, more tolerable, amplitudes. By combining two-photon imaging with behavioral tolerance testing, this work defines a practical therapeutic window for extraocular stimulation of the eye and establishes asymmetric charge-balanced waveforms as a more clinically translatable strategy for visual pathway neuromodulation.

A new electrical stimulation focused computational model of the visual cortex had been created to aid in the development of cortical visual prosthesis. The model consists of 10,666 biophysical neurons representing 0.13mm3 of a layer 2/3 of the primary visual cortex and was calibrated to match the baseline activity of rat brain recordings. A novel model of electrical stimulation was developed to allow for selective activation of specific neuron types, and matched the single cell stimulation response generated by known stimulation models. The electrode was tuned to match recorded population level change in activity across distances and currents recorded in the rats brain. The model is now ready to explore electrical stimulation effects on the visual cortex for examination of neuron specific stimulation to assist in the development of cortical visual prosthesis.

Objective: Adaptive neuromodulation systems and implantable brain-computer interfaces (BCIs) are promising therapies for neurological and psychiatric disorders. However, their broader translation into research and clinical practice remains limited by technological complexity, restricted access to implantable research platforms, and the lack of standardized, reproducible experimental workflows. We therefore aimed to develop and validate an open, general-purpose translational ecosystem that enables rapid development, evaluation, and dissemination of novel neuromodulation and implantable BCI paradigms. Approach: The CorTec Brain Interchange (BIC) implantable neural sensing and stimulation device was integrated with the open-source BCI2000 platform to create a modular, extensible neuromodulation ecosystem. We established a standardized battery of quantitative assessments to characterize implantable neuromodulation systems to comprehensively evaluate the CorTec BIC device through benchtop characterization, long-term preclinical in vitro and in vivo validation, and a human proof-of-concept demonstration. Results: Benchtop and saline testing provided a comprehensive technical ex vivo characterization of the BIC device, independently validating previously reported performance while extending its characterization through quantification of the recording noise floor, stimulation and acquisition latencies and impedance measurement accuracy. Long-term in vivo validation in five canines, with the longest implantation exceeding three years, demonstrated stable chronic recordings while capturing progressive channel deterioration and its underlying mechanical causes. The ecosystem enabled active functional decoding more than two years after implantation, implementation of closed-loop stimulation using arbitrary spectral biomarkers, detection and modulation of epilepsy-associated biomarkers, and brain stimulation evoked potential recordings. In addition, we translated an established one-dimensional BCI cursor control paradigm to the BIC benchtop evaluation kit and demonstrated its feasibility in a human participant. Finally, we openly provide standardized surgical, imaging, and analysis pipelines together with datasets and software to facilitate reproducible neuromodulation research. Significance: We present a versatile, open-source translational ecosystem that supports a wide range of neuromodulation and implantable BCI applications with minimal modification. This battery of quantitative assessments can be applied generally as a blueprint for systematic characterization of implantable neuromodulation systems. By combining comprehensive hardware characterization with standardized software tools and experimental workflows, this work provides both an essential reference for researchers adopting the Brain Interchange platform. The ecosystem lowers technical barriers to implantable neurotechnology research, promotes reproducibility, and provides a foundation for accelerating the development and clinical translation of next-generation adaptive neuromodulation and implantable BCI therapies for patients with neurological and psychiatric disorders.

ObjectiveSpinal evoked compound action potentials (ECAPs) provide a quantitative measure of the neural response during spinal cord stimulation (SCS) and can be leveraged in closed-loop applications to control dose in response to spinal cord movement. However, interpretation of ECAPs recorded in vivo is limited by susceptibility to noise, inter-subject variability, and other confounding factors. As SCS systems evolve and the clinical and research applications of ECAPs expand, it is critical to understand how physiological and technical factors influence ECAP generation and morphology. ApproachWe used a computational modeling framework to systematically investigate the influence of anatomical (e.g., dorsal cerebrospinal fluid (dCSF) thickness), stimulation (e.g., pulse width, waveform shape, stimulation configuration, stimulation frequency), and recording configurations on the neural responses and ECAPs generated during SCS. We employed a hybrid computational modeling approach, coupling finite element method models with multicompartment axon models to simulate neural responses to SCS. Using these models, we characterized the spatiotemporal dynamics of neural recruitment and the resulting ECAP waveforms. Main resultsNeural responses and model ECAPs were strongly influenced by factors, such as dCSF thickness, pulse width, and stimulation waveform shape. Stimulation parameters introduced trade-offs between axonal recruitment thresholds, neural activation selectivity, and ECAP timing and morphology. Notably, similar ECAP amplitudes could obscure differences in the underlying neural recruitment. Complex ECAP morphologies also emerged in response to distinct stimulation paradigms, reflecting changes in the spatiotemporal properties of axonal activation. Additionally, we demonstrate that the selection of recording electrodes can be optimized to enhance recorded ECAP amplitudes. SignificanceOur findings provide a theoretical framework to advance our mechanistic understanding of SCS-induced ECAPs and offer insights into optimization strategies to improve closed-loop SCS therapies.

jaxon is an open-source, fully differentiable and GPU-native reimplementation of the canonical peripheral-nerve fiber models in JAX/Jaxley: the myelinated McIntyre-Richardson-Grill (MRG) and Sweeney axons and the un-myelinated Sundt and Rattay C-fibers. It reproduces NEURONs extracellular mechanism through a custom backward-Euler coupled intracellular/periaxonal double-cable solver, agreeing with PyFibers-wrapped NEURON on 99.6% of 943 activation-threshold configurations within 1% and matching conduction velocity to machine precision. Because the entire forward model is expressed in JAX, it is both vectorized--simulating whole fiber populations in parallel and reaching a geometric-mean [~]820x speedup at N = 100,000 fibers on a single GPU--and differentiable, so extracellular-stimulation parameters (per-contact amplitudes, waveform shape, and electrode position) can be optimized directly through the cable equation rather than grid-searched. jaxon slots into existing peripheral-nerve modeling pipelines as a gradient-enabled, population-scale replacement for the NEURON forward solver.

Objective. Neural decoding performance depends strongly on neural signal representation; however, how the informative components of broadband intracortical activity vary with electrode configuration and decoding strategy remains unclear. We systematically compared the neural features spanning multiple temporal scales to identify the factors underlying robust motor decoding. Approach. Neural recordings were obtained from the dorsal premotor cortex, primary motor cortex, and dorsolateral prefrontal cortex of rhesus monkeys performing delayed-reaching tasks toward static or moving targets. Signals were recorded using 64-channel linear S-probes or 96-channel planar Utah arrays. We compared spiking-band power (SBP), local motor potential (LMP), and local field potential (LFP) power across multiple frequency bands using linear and nonlinear decoders. We further characterized the spectral and temporal components contributing to LMP decoding and evaluated the channel redundancy. Main results. The decoding performance of the SBP and high-frequency LFP features scaled with the number of recorded single units, whereas the LMP performance was more strongly influenced by the electrode geometry. LMP achieved a higher decoding performance with planar Utah arrays than with linear S-probes, accompanied by lower inter-electrode correlations and greater variability in preferred directions. Spectral analysis showed that LMP decoding was dominated by low-frequency components overlapping the delta band and movement-related cortical potentials, with an optimal decoding range of approximately 0.1-8 Hz. Nonlinear temporal decoders, particularly the convolutional neural network and gated recurrent unit models, further improved LMP decoding. Significance. These findings demonstrate that the neural features most informative for motor decoding depend on both the spatial sampling configuration and the temporal structure of the recorded activity. The results highlight the importance of low-frequency population dynamics and provide principles for optimizing neural-signal representations and reducing redundant neural data for scalable and wireless brain-computer interfaces.

Combining complementary neurophysiological modalities offers a promising strategy for improving motor imagery (MI) brain-computer interfaces (BCIs), but learning shared representations across modalities remains largely unexplored. Here, we propose a two-phase deep learning framework for multimodal EEG-MEG decoding that explicitly decouples representation learning from downstream classification. In the first phase, a convolutional encoder-decoder learns a shared latent representation by predicting the power spectral density (PSD) of EEG and MEG signals directly from time-domain activity, rather than using the conventional objective of reconstructing the input signal. In the second phase, the encoder is frozen and its learned representations are reused, without further adaptation, to perform the classification of the downstream MI-BCI task. The framework was evaluated on simultaneous EEG and MEG recordings from 20 participants. The learned representations consistently outperformed conventional handcrafted spectral features, increasing median classification accuracy from 0.734 to 0.794. The multimodal framework also improved performance over MEG alone (median accuracy from 0.680 to 0.794) and yielded a modest increase over EEG alone (median accuracy from 0.765 to 0.794), providing a more robust decoding strategy than single-modality approaches. Furthermore, the learned latent representations were transferable across participants, with more than half of cross-subject models performing within 0.01 accuracy of their subject-specific counterparts. These findings demonstrate that task-agnostic representation learning can capture physiologically meaningful multimodal neural representations that remain transferable across individuals, offering a promising foundation for more robust and reusable BCI pipelines.

Deep brain stimulation (DBS) modeling relies heavily on biophysical neuron models to estimate neural activation thresholds and predict stimulation spread. In this study, we systematically compared a widely adopted axon model, the McIntyre-Richardson-Grill (MRG) model (Model I), with a more detailed biophysical model, the Cohen model (Model II), to assess how structural and electrophysiological differences affect predicted DBS outcomes. Electric field distributions generated by 2202 DBS lead were applied to the neuron models as extracellular input stimuli. Both models were simulated under biphasic pulse stimulation across varying axon-electrode distances, pulse widths, and stimulation frequencies. Activation distances ranged from approximately 2 to 10 mm depending on stimulation parameters and contact location. At 2 mA, Model I achieved an activation distance of 6 mm, whereas Model II reached 10 mm, indicating greater excitability. Across matched fiber tracts, threshold differences ranged from -1.40 mA to 0.27 mA, with Model II requiring lower thresholds in 97.7% of cases. Both models showed a strong inverse relationship between pulse width and activation threshold. However, frequency responses differed: Model II exhibited increasing thresholds at higher frequencies, while Model I showed a slight decrease. Machine learning regressors trained on distance, pulse width, and frequency achieved high predictive accuracy, with Gradient Boosting performing best. Model II demonstrated superior prediction metrics (R^2 = 0.986; RMSE = 0.045 mA; MAE = 0.034 mA) compared to Model I (R^2 = 0.977; RMSE = 0.089 mA; MAE = 0.068 mA). Overall, both models reliably estimate DBS-induced activation, but structural differences significantly affect excitability and frequency-dependent behavior. With appropriate awareness of their respective strengths and limitations, either model can be used to derive activation distances for estimating electric field isolevels and the volume of tissue activated in patient-specific DBS simulations.

Surface electromyography (sEMG) is a robust non-invasive modality for human-machine interaction, yet its application remains largely limited to coarse motor tasks such as grasping or rotation. The decoding of fine motor skills, specifically handwriting, remains a challenging problem with potential relevance for prosthetic control and natural communication interfaces. In this work, we explore a Transformer-based alternative to classical signal-processing pipelines that treats multi-channel sEMG signals as complex time series. We introduce DualMyo, a specialized model integrating Patch Embeddings and Rotary Positional Embeddings (RoPE) to capture the intricate spatio-temporal dynamics of myoelectric activity. Our experimental results show strong intra-session performance. Furthermore, we address the inherent challenges of signal drift and sensor displacement in cross-session applications. We show that a lightweight fine-tuning strategy of 10 epochs enables DualMyo to effectively adapt to session variability, achieving approximately 91\% accuracy with two examples per digit. These findings provide a promising step toward adaptive sEMG-based handwriting interfaces, although further validation is required for real-time and multi-subject deployment and neuromuscular control.

Temporal interference (TI) stimulation is a promising non invasive technique for deep brain neuromodulation, yet accurately characterizing its complex and spatially distributed electric fields remains a fundamental challenge. This study proposes a novel computational framework that integrates topological analysis with mesh-aware metrics to overcome the limitations of conventional global field descriptors. The method introduces three interconnected components: (1) connected component , which extracts spatially continuous field regions above thresholds; (2) discrete ellipsoid modeling, which quantifies the geometric centroid, directional spread, and anisotropy of each region; and (3) Local Moran's I to identify robust spatial clusters while suppressing numerical noise. Applied to TI simulations using individualized tetrahedral head models, the framework demonstrates that the few largest connected components above an elevated intensity threshold collectively provide a more accurate representation of the stimulation targets. The approach provides highly interpretable visual outputs that directly map field topology to brain anatomy, including layered component maps, discrete ellipsoids, and spatially weighted clustering landscapes. By offering a topologically coherent, noise robust, and visually intuitive analytical toolbox, this work advances the precision and interpretability of TI field assessment, supporting more reliable target localization, focality quantification, and parameter exploration in translational neuromodulation research.

Electromyography (EMG) is fundamental to clinical assessment, rehabilitation, neuromuscular research, and human-machine interfaces. Despite decades of use, no widely adopted standard exists for organizing and sharing EMG data, limiting reusability and large-scale data aggregation. We present EMG-BIDS, an extension to the Brain Imaging Data Structure (BIDS) that standardizes the organization of EMG recordings. EMG-BIDS addresses challenges unique to EMG, including diverse electrode types (surface or intramuscular, single channel to high-density arrays), heterogeneous electrode placements across anatomical locations, montages (e.g., monopolar or bipolar sensor designs), and the critical need for transparent documentation of sensor positioning. The specification introduces hierarchical coordinate systems that link local electrode grids to anatomical landmarks, enabling precise and reproducible placement documentation. EMG-BIDS is now part of BIDS as of version 1.11.0, supported by existing tools, including MNE-BIDS and EEGLAB. We demonstrate the specification through public datasets, including high-density surface EMG recordings. EMG-BIDS provides the foundation for FAIR (Findable, Accessible, Interoperable, Reusable) EMG data sharing, enabling meta-analyses, multi-site studies, and machine learning applications that require standardized, well-documented datasets.

Beta oscillations (13-30 Hz) are a prominent sensorimotor neural rhythm and an important biomarker in neurorehabilitation. These oscillations propagate along the corticospinal pathway and are expressed in the discharge patterns of spinal motor neurons, enabling the assessment of corticomuscular coupling. Moreover, peripheral beta band oscillations have recently emerged as a potential control signal for motor augmentation interfaces. However, it remains unclear whether peripheral beta activity simply reflects cortical oscillations or is partly shaped by peripheral mechanisms, and to what extent it can be voluntarily controlled. To address these questions, we developed a 10-day neurofeedback protocol in which participants learned to up-regulate peripheral beta band activity. Subjects were trained to exploit movement cancellation, a behaviour naturally associated with increased cortical and muscle beta band activity, as a two-state strategy to voluntarily modulate peripheral beta band power. Each session included a guided familiarization phase based on a GO/NO-GO task, in which participants familiarized with movement cancellation through guided visual cues, followed by an asynchronous control phase in which they self-initiated the same strategy without external guidance to increase peripheral beta band activity in a target window. Participants progressively improved their ability to voluntarily modulate peripheral beta band activity. Peripheral beta band power during movement cancellation increased significantly across training days in both the familiarization and asynchronous control phases. Intramuscular coherence in the beta band also increased, indicating enhanced common synaptic input to the motor neuron pool in this band. In contrast, cortical beta power and corticomuscular coherence remained unchanged. Together, these findings demonstrate that peripheral beta band activity is a dynamic neural feature that can be voluntarily shaped through training, supporting its potential as a non-invasive control signal for future neurorehabilitation and motor augmentation technologies.

Background and Objectives: Code-modulated visual evoked potentials (c-VEPs) provide fast and reliable control signals for reactive brain-computer interfaces (rBCIs). However, deployment beyond laboratories remains limited by visually intrusive stimulation, reliance on wet electroencephalography (EEG), and false activations during non-control periods. We introduce a usability-oriented c-VEP rBCI combining low-salience textured stimulation, slow flashing codes, asynchronous evidence accumulation, and semi-dry EEG acquisition. Methods: Thirty participants evaluated the system on a standard screen using an 11-class keypad typing task. Two identical online typing sessions were separated by a 2-min video-viewing period serving as an explicit idle condition. Feasibility was then assessed in virtual reality (VR) through a proof-of-concept shell-game task involving head movements and immersive rendering constraints. Visual comfort, peripheral distraction, and fatigue were assessed subjectively in both conditions. Results: In the keypad task, the system achieved 99.8% accuracy with 33 s of calibration data and a mean decoding time of 4.3 s. No spurious commands occurred during the tested 2-min idle condition. In VR, online accuracy reached 100%, with a mean decoding time of 5.6 s. Participants reported high visual comfort and minimal peripheral distraction in both conditions, although fatigue was higher in VR. Conclusion: The proposed c-VEP rBCI combines short calibration, high accuracy, comfortable stimulation, semi-dry EEG, and asynchronous decision-making. Performance during the explicit idle condition and VR implementation support the feasibility of wearable and practical c-VEP systems for self-paced interaction beyond laboratory environments. These findings encourage evaluation in longer idle periods and real-world tasks.

Brain-computer interface (BCI) research relies on multistage computational pipelines, but progress has been slowed by fragmented data formats, heterogeneous decoder implementations and hardware-specific deployment toolchains. Here, we introduce BCIJelly, a unified ecosystem that standardizes 18 BCI datasets into AI-ready inputs and integrates 15 benchmark decoders, 80 reusable modules, automated architecture search (AAS) and hardware-aware neuromorphic deployment. Our AAS constructs task-specific decoders without manual design and extends into a large language model (LLM)-driven closed-loop mode supporting single-task, multitask and cross-species decoder design. A single-command pipeline compiles trained decoders for neuromorphic hardware, reducing power consumption by 30 to 50 times while preserving decoding performance. An interactive visualization software enables code-free exploration of neural recordings and decoding outputs. BCIJelly is validated across five BCI paradigms (motor, visual, speech, emotion and auditory) in humans, macaques and mice, providing an extensible ecosystem connecting data standardization, decoder development, systematic evaluation and hardware-aware deployment for BCI research.

Dexterous hand control is essential for daily activities and a top restoration priority for people with tetraplegia, yet neurophysiological datasets suitable for studying and decoding naturalistic hand behavior remain scarce. Most available recordings constrain movements to a small number of degrees of freedom or cover only a single stereotypical grasp-and-hold structure. Moreover, previous brain-computer interface (BCI) studies have largely relied on signals from a single cortical area, even though dexterous hand control is distributed across an interconnected frontoparietal network. To address these limitations, we designed a paradigm that elicits rich hand movements across 40 distinct hand-object interaction conditions. We recorded simultaneous frontoparietal activity with hand kinematics from a rhesus macaque performing them. Neural activity was recorded from the primary motor cortex, anterior intraparietal area, and ventral premotor cortex using microelectrode arrays spanning 1,920 channels, while a multi-camera markerless motion-capture system acquired video from which kinematics were extracted offline. The dataset comprises data from 20 sessions, including processed neural features, synchronized 3D coordinates of hand and elbow keypoints, and trial-level metadata including task conditions and event timing. We further provide technical validation, including a neural decoding benchmark with multiple algorithms for continuous keypoint coordination reconstruction. We found that cross-day and cross-task decoding performance varied among algorithms. Variation in channel availability leads to different performance degradation curves in different algorithms. This task-rich dataset offers a valuable resource for investigating the neural mechanisms of dexterous hand control and for advancing the performance of motor BCIs.

Electrocutaneous stimulation provides a means to warn workers of potential hazards. Our previous parameter studies were conducted at rest or under controlled external influences such as vibration, temperature, and humidity, whereas actual work tasks have not yet been addressed. As a step towards greater practical applicability, we conducted two studies in which a circumferential electrocutaneous warning signal was applied to the upper right arm while participants performed a reading task, screw-driving, and polishing. In study 1 (n = 32), the pulse interval was adjusted individually to evoke a vibrating sensation, and the intolerance threshold was determined. In study 2 (n = 29), a fixed pulse interval of 36 ms was applied to all participants, the warning threshold was determined in addition to the intolerance threshold, and muscle twitches were documented both by participant report and by an independent observer. Study 1 demonstrated that electrocutaneous warning during work tasks is feasible, with a median intolerance threshold higher during polishing (16.5 mA) than during reading or screw-driving (14 mA each). In study 2, the fixed parameter setting elicited the intended pulsating or vibrating sensation at rest in 89 % of the participants in both presentations. The median warning threshold was 8 mA and did not differ between the three work tasks, whereas the intolerance threshold was lower during reading (18 mA) than during screw-driving and polishing (20 mA each). Muscle twitches were largely absent at the warning threshold and became frequent towards the intolerance threshold, with a ventral dominance of their location in both studies. An individual parametrization is therefore not required for the majority of users when a binary warning is to be conveyed, and operating the system slightly above the warning threshold provides a usable amplitude range while minimizing muscle twitches. Future work will focus on electrode optimization and on the objective measurement of motor responses.

Surface electromyography (sEMG) offers a silent and wearable input modality, but its practical use is limited by variability across users and recording sessions. This study presents a compact CNN- Transformer model for decoding isolated handwritten digits from eight-channel sEMG signals. The model combines trainable signal preprocessing, convolutional feature extraction, and Transformerbased temporal modeling. It was evaluated on ten recordings from five participants using recordingseen classification, leave-one-recording-out (LORO) generalization, and few-shot adaptation. The model achieved a mean macro F1 score of 0.924 {+/-} 0.059 in the recording-seen setting and 0.619 {+/-} 0.252 under zero-shot LORO evaluation. Adaptation using two labeled trials per digit increased macro F1 to 0.828 {+/-} 0.112, while ten trials per digit achieved 0.925 {+/-} 0.053. The proposed architecture also outperformed classical and neural baselines in the controlled LORO benchmark. These results indicate that compact CNN-Transformer models, combined with lightweight target-recording calibration, provide a promising basis for adaptive sEMG-based input systems.

From a statistical point of view, clustering spike time-windows from multi-electrode arrays (MEAs) recordings is a challenging high-dimensional, unsupervised clustering task where stationarity, commonly assumed in standard time-series analysis, is often violated and for which a gold standard is currently unavailable. Here we aim at providing practical guidance on how to cluster this type of data by systematically comparing 108 clustering pipelines differing along three main dimensions: (i) the data feature space; (ii) the metrics used to quantify distance between data points; and (iii) the clustering algorithm. For our benchmark we used both labeled synthetic data, mimicking four physiologically-inspired classes of spike time-windows, and a set of real MEAs recordings from a two-dimensional in-vitro neural culture. The performance of the competing pipelines was evaluated in terms of balanced accuracy and computational time when analyzing the synthetic datasets, while the Silhouette score was used for the real dataset, where no ground-truth is available. Overall, our analysis shows that the best combination is formed by k-means with Euclidean distance applied after Principal Component Analysis (PCA) of the spike time-windows. Conversely, hierarchical clustering showed the highest computational burden, while Independent Component Analysis and kernel PCA provided less effective noise suppression.