Journal of Neural Engineering

Brain-Computer interfaces (BCIs) offer a link between neural signals and external computation, enabling control of devices for the purposes of restoring function to motor-affected individuals and enhancing capabilities of a wider set of populations. Electroencephalography (EEG) offers a high temporal resolution for dynamic and potential real-time feedback for non-invasive systems. However, its practical efficacy remains limited due to low spatial resolution and poor signal-to-noise ratio, leading to insufficient decoding accuracy and unintuitive control paradigms that hinder reliable user interaction. In this study, we present a framework for an online EEG foundation model by creating a custom foundation model through spectrogram reconstruction of compact temporal windows and online constraints during pretraining. We evaluate the performance of the model in a challenging control paradigm of single-arm, directional motor imagery with dynamic movements for guided and free movement cursor control tasks. Our foundation model approach achieved a final average accuracy of 51.3% during a goal-oriented guided control task. This represents a 15.8% increase over a conventional deep learning framework and a 26.3% increase above chance level, evaluated in a cohort of 11 human participants. During the free movement task, the foundation model invoked a higher rate of completion and lower completion times. Furthermore, the custom EEG foundation model demonstrated superior adaptability from same-session finetuning and indicated an enhanced capability to assist subject learning. These findings highlight the potential of EEG foundation models to support more robust and intuitive non-invasive BCI systems, providing a promising modelling framework for future BCI development.

Vagus nerve stimulation (VNS) is an established clinical therapy for drug-resistant epilepsy and shows potential for treating other conditions, including depression, rheumatoid arthritis, diabetes, and heart failure. However, stimulation often produces unwanted side effects such as hoarseness, coughing, and paraesthesia. In some cases, these effects limit the delivery of therapeutic stimulation levels and hinder the development of new neuromodulation therapies. Selective VNS (sVNS) offers a strategy to reduce off-target organ activation. Methods. This work presents an NFC-controlled, wirelessly powered, battery-free, temporary implantable multichannel stimulation device, made with off-the-shelf components, enabling selective stimulation of specific regions of the VN. The encapsulated device is suitable for short-term implantation in animals. Main result. The sVNS device was investigated in a porcine (n = 4) trial and an n = 1 pilot human experiment. Selective bradycardia of 23.28 {+/-} 12.91% was achieved in pigs and 7.5% in the human participant. In humans, a clear separation of cardiac efferent and afferent stimulation was observed, with additional selectivity in laryngeal activity. Physiological effects of laryngeal and cardiac fibre separation were measured to be 231{degrees}. Significance. Geometrically selective stimulation of VN fascicles has the potential to improve clinical outcomes, enhance therapeutic efficacy, and reduce stimulation-related side effects. This strategy may enhance neuromodulation approaches for the treatment of heart failure using VNS.

ObjectiveConvolutional neural networks (CNNs) have shown promise in decoding neural drive from high-density surface electromyography (HD-sEMG) signals. However, the effects of convolutional kernel dimensionality on the generalizability and computational efficiency of CNN-based neural drive decoding remain unclear. This study systematically examined how the dimensionality of convolutional kernels (1D, 2D, and 3D) affects both the generalizability and computational efficiency of CNN-based neural drive decoding. ApproachThree CNN architectures differing only in the dimensionality of their convolutional kernels were implemented to extract temporal (1D), spatial (2D), or spatiotemporal (3D) features from HD-sEMG signals of isometric knee extension, ankle plantarflexion at three intensities. Each CNN was repeatedly trained using subsets of a pooled training dataset with varying sizes. Cross-intensity and cross-muscle generalizability were assessed by the correlation coefficient between neural drive from deep CNN and that from golden standard blind source separation (BSS) algorithms. Computational efficiency was assessed by measuring inference time on both CPU and GPU platforms. Main ResultsAll CNN architectures demonstrated generalizability across contraction intensities and muscles. For cross contraction intensities, the 1D, 2D, and 3D CNNs achieved mean correlation coefficients of 0.986 {+/-} 0.009, 0.987 {+/-} 0.010, and 0.987 {+/-} 0.010, respectively. For cross-muscle generalizability, the corresponding correlation coefficients were 0.961 {+/-} 0.051, 0.965 {+/-} 0.049, and 0.968 {+/-} 0.046. In terms of efficiency, the 3D CNN was the least computationally efficient, with inference times of 4.1 ms per sample on the CPU and 1.2 ms per sample on the GPU. SignificanceThese findings demonstrate that increased CNN architectural complexity does not necessarily yield superior generalizability in neural drive decoding from HD-sEMG signals. The results provide practical guidance for balancing decoding performance and computational efficiency in HD-sEMG-based neural-machine interfaces.

BackgroundNon-invasive neuromodulation technologies have advanced considerably. Yet, precise and focal activation of deep brain regions remains challenging due to the rapid attenuation of electric fields across the scalp, skull and brain surface. ObjectiveWe present FLOATES (FLOAting Transcranial Electrical Stimulation), a novel approach that employs an untethered wire implanted in the brain which passively relays currents injected transcranially from the brain surface to deep brain regions, achieving focused stimulation deep within the brain. MethodsWe validated FLOATES through a combination of simulations, benchtop testing, and in vivo rodent studies. The benchtop experiments confirmed the ability to relay the field across the floating wire. Rodent studies demonstrated capability to stimulate deep brain regions in vivo. ResultsOur simulation and benchtop testing results indicate that FLOATES can deliver significantly higher electric fields to subcortical regions compared to conventional transcranial stimulation approaches. Further in-vivo results demonstrated deep subthalamic nuclei stimulation to evoke limb motor responses and demonstrated a significantly lower motor threshold compared to transcranial stimulation. Finite element simulations reveal that the efficiency of FLOATES depends on several key parameters including input field strength, wire length and diameter, exposed electrode area, impedance, and tip geometry. Simulations using a human-sized head model suggest that electric fields sufficient for brain stimulation can be obtained with reasonable currents injected to the scalp. ConclusionTogether, these results establish a theoretical and experimental foundation for FLOATES as a minimally invasive and spatially precise brain stimulation platform in modulating deep neural circuits implicated in neuropsychiatric and movement disorders.

Modern neuroelectronic interfaces have shown great potential to diagnose conditions, address neurological dysfunction, and advance neuroscientific knowledge. However, neural interface systems today require tethered connections that restrict mobility, prevent testing across ecological contexts, and inhibit clinical translation to at-home use. Fully implantable commercial systems have previously been developed, but exhibit significant constraints, including a bulky design, limited modularity, low bandwidth, or unidirectional communication (e.g. deep brain stimulation systems, DBS; spinal cord stimulation systems, SCS). Here, we have developed the Modular Bionic Interface (MBI), a system composed of a fully implantable device and a worn unit for high-bandwidth, bidirectional interfacing with the nervous system. The MBI can record high fidelity electrophysiological signals and deliver spatiotemporally modulated electrical stimulation for clinical and research purposes through flexible interaction with third party implantable devices. We performed benchtop evaluation to validate the recording and stimulation capabilities of the MBI across a diverse range of inputs and outputs. We then evaluated the MBI system in vivo through chronic implantation within a sheep, where results were stable for the length of evaluation, over three months. While connected to an actively powered, third-party high-resolution spinal cord stimulation electrode array, the MBI system was able to deliver stimulation to evoke lower extremity motor responses and record spinal compound action potentials evoked by peripheral nerve and spinal stimulation. Through rigorous evaluation, we demonstrate a fully implantable system with a small footprint capable of high-resolution, bi-directional communication with the nervous system via modular connections to third-party devices. We expect that modular devices will further our ability to treat complex neurological disease and injury.

Intracortical brain-computer interfaces (BCIs) are used to decode motor intent from neural population activity; their main clinical application is to restore function for individuals with motor or communication deficits. However, when trying to reconstruct movement trajectories, such as in computer cursor control, even state-of-the-art decoders fall short of able-bodied performance during online BCI control. This calls for alternative approaches to improve the usability of motor BCIs. Here, we leveraged an error signal, i.e. a neural correlate of faulty motor control that can be detected from neural activity. By detecting this error signal in parallel to performing movement decoding, it is possible to perform error modulation, i.e. real-time error detection and correction during a closed-loop motor BCI task. We analyzed data from four individuals with upper limb impairment due to cervical spinal cord injury who each used an intracortical BCI to perform a continuous cursor control task with visual feedback. A classifier was trained to detect the error signal and was used to perform online error detection during BCI control to limit ongoing errors (defined as movement of the controller away from its target) without requiring any specific action from the participants. Our contribution is three-fold. First, we show that the error signal has a pre-error component. Cortical activity was already significantly modulated before the onset of the kinematically-defined error, theoretically allowing for earlier detection. Second, we show that error modulation significantly improves performance during online BCI control of cursor kinematics. Finally, we show that the error signal can be robustly leveraged across contexts, as error modulation improves performance in more complex motor tasks (involving for instance grasp and drag actions) or other environments without task-specific calibration. Overall, our results suggest that the error signal can be robustly disentangled from motor intent in cortical activity, and that even a simple linear classifier can enable error modulation in parallel to a continuous kinematic decoder, yielding more reliable and accurate BCI control.

Motor imagery (MI) brain-computer interfaces (BCIs) are promising technologies for neurorehabilitation. In this context, deep learning (DL) models are increasingly being used to decode the mental imagination of movement. However, countless studies across multiple domains have shown that DL models are susceptible to bias, which can lead to disparate performance across subpopulations in terms of protected attributes, such as sex. The reported presence of sex-related information in electroencephalography (EEG) signals, widely used for MI-BCI, further raises warnings in this regard. For this reason, we conducted an in-depth analysis of the performance of DL in terms of the sex and other potential confounding factors. While an initial basic stratified analysis in terms of sex showed differences in favor of the female population, further analysis revealed that performance disparities were actually primarily driven by the discriminability of EEG patterns themselves, and not by the DL model. Moreover, DL models improve overall performance as well as per-group performance, particularly helping subjects with less discriminable EEG patterns. Our work highlights the benefits of DL methods for MI-BCI as well as the need for careful analysis when it comes to bias assessment in complex settings where multiple variables interact. We argue that in-depth studies of model behavior beyond standard performance metrics, should become widespread in the community in order to ensure the development and later deployment of fair BCI systems.

Brain-computer interfaces (BCIs) translate brain signals into commands for external devices, with motor imagery (MI) BCIs decoding imagined movements to aid neurorehabilitation. Although high-channel EEG offers rich data, such systems are bulky and impractical for everyday use. This study assesses whether a low-channel, consumer-grade headset (Muse) can match a clinical-grade system (OpenBCI) in classifying lower limb MI and motor execution (ME). Six healthy volunteers performed left and right knees and ankles MI and ME tasks while EEG was recorded concurrently from both devices. Signals were band-pass filtered (8-30 Hz), segmented into overlapping one second windows, and features were extracted across time, frequency, and time-frequency domains. Feature dimensionality was reduced via mutual information-based minimum redundancy maximum relevance and principal component analysis. Five classifiers (support vector machine, linear discriminant analysis, k nearest neighbors, random forest, and AdaBoost) were applied to nine binary discrimination scenarios and evaluated with 10-fold cross-validation via 100 Monte Carlo iterations. Frequency domain features, particularly those derived from Welchs power spectral density, were most frequently selected. Mutual information analysis indicated that C3 and C4 electrodes were most informative for OpenBCI, while in Muse, the channels contributed more evenly, except in laterality classification scenarios, where TP9 played a key role. OpenBCI outperformed Muse in classifier-based accuracy with superiority ranging from 0.4% to 4.8%, while task-based differences were more variable, ranging from -0.3% to 8.7%. Despite its lower spatial resolution, the Muse system achieved competitive performance, especially in motor vs. rest tasks, and shows promise as an affordable, user-friendly alternative for home-based neurorehabilitation BCIs.

ObjectiveComputational models and visualization toolboxes for Deep Brain Stimulation (DBS) increasingly rely on pre-computed electric field libraries to estimate the Volume of Tissue Activated (VTA). However, the boundary conditions (BCs) and source models used to generate these fields vary widely across studies, and there is currently no experimental consensus regarding which parameters most accurately reflect the physical device output. The objective of this study was to experimentally validate the electric potential distribution of directional DBS leads in order to determine the optimal Finite Element Method (FEM) configuration. ApproachThe voltage distribution surrounding a Boston Scientific Vercise Gevia directional lead was mapped in a saline phantom using a custom high-precision robotic scanning system. Experimental measurements were compared against six FEM configurations that varied in source formulation (Dirichlet vs. Neumann boundary conditions) and ground definitions. For each configuration, the resulting VTA volume was computed to assess the clinical impact of modeling assumptions. ResultsThe FEM configuration implementing a Dirichlet (voltage) boundary condition on the active contact with a grounded implantable pulse generator (IPG) surface demonstrated the highest accuracy, achieving a Symmetric Mean Absolute Percent Error (SMAPE) of less than 9% across all contact levels. In contrast, conventional current-controlled simulations employing Neumann boundary conditions with disparate ground definitions substantially overestimated electric field spread. Suboptimal boundary condition selection resulted in an approximate 67% overestimation of VTA volume (137 mm3 vs. 82 mm3) relative to the experimentally validated model. SignificanceAlthough clinical DBS systems operate as current sources, standard Neumann (current density) boundary conditions do not adequately represent the equipotential behavior of the electrode-tissue interface, resulting in nearly a two-fold error in predicted VTA volume. To improve the validity of predictive clinical models, we recommend the use of Dirichlet boundary conditions derived from the device operating impedance (V = Itarget x Zmeasured) rather than conventional current density specifications.

ObjectivesEpidural spinal cord stimulation (SCS) is an emerging therapy for motor rehabilitation following spinal cord injury (SCI) and other motor disorders. Conventionally, SCS leads are placed along the dorsal spinal cord (SCSD), where stimulation activates large diameter afferent fibers, which indirectly activate motoneurons through reflex pathways. This leads to broad activation of flexor and extensor muscles and limited fine-tuned control of motor output. Targeting the ventral spinal cord (SCSV) may enable more direct activation of motoneuron pools, potentially improving the specificity of muscle activation; however, there is currently no established method to place leads ventrally. To address this, we evaluated the feasibility of four modified percutaneous implantation techniques to target the ventrolateral thoracolumbar spinal cord. Materials and methodsPercutaneous SCSV implantation was performed in three human cadaver torso specimens under fluoroscopic guidance. The following approaches were evaluated: sacral hiatus, transforaminal, interlaminar contralateral, and interlaminar ipsilateral. The leads in the latter 3 approaches were inserted between L1 and L5. Eighteen implants were attempted, with nine leads retained for analysis. Lead and electrode position were assessed using computed tomography (CT) with three-dimensional reconstruction, along with anatomical dissection to verify lead and electrode placement within the epidural space. ResultsSuccessful ventral epidural lead placement was achieved using all four implantation approaches. The sacral hiatus (16/16 electrodes) and transforaminal (8/8 electrodes) approaches resulted in exclusively ventrolateral placement. The interlaminar contralateral approach led to 27/32 electrodes positioned ventrolaterally and 5/32 dorsally. The interlaminar ipsilateral implantation approach led to 14/32 electrodes positioned ventrolaterally and 18/32 positioned ventromedially. ConclusionsThese findings demonstrate that ventral epidural SCS lead placement can be achieved using modified percutaneous implant techniques. The four approaches outlined here provide a clinically feasible pathway to SCSV and establishes a foundation for future clinical studies investigating SCSV for motor rehabilitation following SCI.

ObjectivesDeep brain stimulation (DBS) is an established therapy for neurological disorders such as Parkinsons disease (PD). Modern DBS devices can record local field potentials (LFPs) to guide DBS therapy. LFPs from these devices are typically limited to bipolar configurations to suppress common-mode noise and reject artifacts. However, bipolar recordings also attenuate some local physiological signals. Methods that convert bipolar to monopolar power offer more spatially precise estimates of LFPs. Herein, we develop a model to estimate monopolar power from bipolar recordings. Materials and MethodsThis retrospective study analyzed 64 patients with PD undergoing STN (11) or GPi (53) DBS implantation. Intraoperatively, LFPs were recorded from all contacts and filtered. Bipolar montages were generated for each combination. Power spectral density (PSD) was calculated from each monopolar and bipolar signal, averaged over canonical frequency bands, and processed as log PSD. A common set of bipolar configurations was selected to minimize the Condition Number (CN), maximizing model stability. Monopolar and bipolar powers were related using robust OLS regression. Observations were randomly partitioned into training and validation sets. ResultsSixty-four PD patients yielded 640 observations. The configuration group with the lowest CN (7.45) was {C03, C12, C23}. The models demonstrated adjusted R2s of 0.9015, 0.9055, 0.8853, and 0.8764, and RMSEs (dB) of 3.2663, 3.2801, 3.5815, and 3.7035 when predicting C0, C1, C2, and C3 (N = 500; all p < 0.0001). The STN, GPi, and combined cohorts performed comparably. Weights transferred from the combined model to the validation set retained high performance. ConclusionsThis study demonstrates that monopolar LFP power can be accurately estimated from bipolar power using a linear regression model with strong generalizability across targets and validation sets. This approach offers a hardware-agnostic solution to spatially disambiguate signals and better inform DBS programming and adaptive stimulation in chronically implanted devices.

Online processing and visualization of large-scale neural data is critical for neuroscientific discovery and advancements in neural engineering. However, with the development of technologies like Neuropixels (NP) probes, which enable simultaneous streaming from hundreds of recording electrodes, handling such data in real-time has become an ongoing challenge. Moreover, keeping pace with recording hardware has required most existing software, such as SpikeGLX for NP probes, to prioritize acquisition stability, leaving data processing and visualization to primarily be performed offline. Thus, we created OP-GLX, a MATLAB-based toolbox designed to operate in tandem with SpikeGLX to enhance the fetching, processing, and visualization of incoming neural data. The OP-GLX toolbox features several processing capabilities, including spike detection, computing time-binned firing rates, plotting spike waveforms, and conducting principal component analysis (PCA). The processed neural data is displayed on a native graphical user interface (GUI) for intuitive and customizable interaction with the experiment. The performance testing of OP-GLX showed that it supports real-time operation, confirmed by the absence of SpikeGLX stream buffer fetch errors across multiple acquisition settings. By complementing current neural data acquisition methods and providing stable online functionality, we envision that OP-GLX will enable researchers to visualize and interpret their data more effectively during ongoing neuroscience experiments.

BackgroundLower-limb EEG is a rehabilitation-facing control signal for stroke neurorehabilitation and future non-invasive brain-spine interfaces, but a public external benchmark that jointly audits source construction, minimal adaptation burden, and confound sensitivity is lacking. We therefore tested whether lower-limb effort-versus-rest decoders trained on healthy public EEG transport to a stroke target domain. MethodsWe conducted a retrospective public-data external benchmark using three public EEG datasets harmonised to a common lower-limb effort-versus-rest target. Classical and deep models were compared under zero-shot transport, 10-shot temperature calibration, and 10-shot fine-tuning. For few-shot analyses, each target participant contributed a trial-disjoint subject-internal support set of 10 labelled trials per class and a held-out remainder test set. Prespecified analyses audited source construction, support-resampling sensitivity, and montage controls. Uncertainty was summarised with participant-level bootstrap confidence intervals. ResultsWithin this benchmark, healthy-to-stroke zero-shot transport was weak. The best zero-shot result was classical rather than deep, with CSP+LDA reaching area under the receiver operating characteristic curve (AUROC) 0.603, whereas EEGNet remained near chance (AUROC 0.527). Ten-shot calibration improved operating behaviour more than discrimination: for CSP+LDA, expected calibration error fell from 0.267 to 0.035 and specificity increased from 0.180 to 0.485, whereas AUROC remained essentially unchanged (0.603 to 0.604). Ten-shot fine-tuning produced only modest gains; the best overall AUROC was 0.605 for pooled dataset-balanced CSP+LDA, numerically tied with pooled raw CSP+LDA (0.605). MILimbEEG-only source training was consistently weak, exploratory deep domain-generalisation variants did not rescue transport, and frontal and temporal montage controls remained relatively competitive. ConclusionsWithin this public benchmark, source construction and minimal adaptation burden mattered more than model novelty, and retrospective montage controls limited motor-specific interpretation. The results support harmonised prospective validation of lower-limb EEG transport over further retrospective model iteration.

Neural decoders serve as probabilistic interfaces in co-control brain-to-text BCIs, where predicted uncertainty shapes hypothesis generation and language model integration, enabling decisions to be made safely under uncertainty. However, it remains unclear whether these decoders produce reliable and informative uncertainty, or how training objectives shape these properties. This work characterizes and improves uncertainty representations in brain-to-text decoding. We extend two metrics, calibration error (ECE) and resolution (RES), to evaluate sequential probabilistic predictions from frame-level phoneme estimates to word-level hypotheses, quantifying the reliability and informativeness of model uncertainty. Using this framework, we analyze neural decoders trained with connectionist temporal classification (CTC). To isolate the causal role of uncertainty independent of accuracy, we manipulate predicted probability distributions while holding predicted sequences fixed. Motivated by the observed failures, we further examine the role of the training objective and propose a two-stage cross-entropy (CE) formulation that decouples alignment inference from classification. We show that widely used CTC-trained neural decoders in brain-to-text BCIs produce systematically over-confident predictions, with high confidence persisting even when predictions are incorrect. Controlled manipulations of the prediction reveal that improved ECE and RES enhance hypothesis generation and language-model integration by promoting diverse alternatives and more effective re-ranking of hypotheses aligned with user intent. Mechanistically, CTC relies on over-confident predictions to resolve alignment ambiguity. Replacing CTC with CE loss yields significantly more reliable and informative probabilistic predictions without degrading decoding accuracy. Uncertainty emerges as a system-level design variable in brain-to-text interfaces. Calibrated uncertainty from neural decoders enables effective integration with independently trained language models and reliable error detection. This work reframes uncertainty from a passive output into an active control signal, identifies key components and evaluation criteria for probabilistic co-control, and outlines a pathway toward next-generation BCIs that supports increasingly complex interactions with the world.

BackgroundPersonalized optimization of 4x1 high-definition transcranial electrical stimulation (HD-tES) faces inherent trade-offs between montage flexibility, computational efficiency, and implementation accessibility. Conventional 10-10 electrode systems constrain placement to discrete landmark positions, while unconstrained optimization relies on stochastic algorithms that risk converging to local optima and requires neuronavigation equipment often unavailable in rehabilitation settings. Here we introduce a scalp geometry-based parameter space (SGP) that parameterizes 4x1 HD-tES montages using three intuitive scalp-defined parameters--position, radius, and orientation--and characterize parameter-performance regularities through exhaustive electric field simulations across 30 subjects and 624 cortical targets (>3.6 million configurations). ResultsPosition primarily determined proximity to optimal performance, radius governed the intensity-focality trade-off, and orientation served as fine-tuning. Exploiting these regularities, a minimal search space (SGP-MSS) was constructed that reduced computational complexity by over 90% while guaranteeing global optima identification. Compared with standard 10-10 montages, SGP-MSS achieved up to 99% higher targeting intensity and 126% higher focality (all p < 0.0001). Compared with lead-field-free optimization, SGP-MSS achieved comparable performance with greater cross-subject stability. ConclusionsThe SGP framework enables efficient individualized HD-tES optimization without neuronavigation. Its scalp-based parameterization supports electrode positioning via standard cranial landmark measurements, facilitating translation to routine clinical and home-based rehabilitation settings.

Conventional temporal interference stimulation (TI, TIS, or tTIS) leverages two pairs of electrodes to induce an interfering electrical field in the brain. Both computational and experimental studies show that TI can stimulate deep brain regions without significantly affecting shallow areas. While promising, optimization of the locations and dosages on these two pairs of electrodes for maximal focal modulation remains computationally challenging. We are the first to propose two arrays of electrodes instead of two or multiple pairs of electrodes to boost modulation focality. However, the optimization algorithm outputs too many electrodes with overlaps across two frequencies, making it difficult to implement in practice. Based on recent progress in developing multi-channel TI devices and computational work on TI optimization, here we again advocate two-array TI, but with solid software and hardware evidence to show the feasibility. Specifically, we show that the latest optimization algorithm for two-pair TI innately works for two-array TI with the fastest speed (under 30s) among all major algorithms. With a similar amount of electrodes, two-array TI could achieve better focality (3.03 cm) at the hippocampus even than TI using up to 16 pairs of electrodes (3.19 cm) that takes days to optimize. We also show a hardware implementation of two-array TI using 10 electrodes on our 8-channel TI device. We argue that two-pair TI is only preferred when one does not care about modulation focality and promote two-array TI for its advantages in focality and lower cost in terms of both optimization time and electrodes needed. We restate the focality-intensity tradeoff but in the context of TI and provide a first voxel-level map of achievable focality and modulation strength by TI in the MNI-152 head template. We hope this work will pave the way for future adoptions of two-array TI for more focal non-invasive deep brain stimulation.

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) has strong potential for recording cortical reactivity and connectivity. However, this promise is hampered by TMS-induced EEG artifacts. Here, we examine the origins of these artifacts with phantom TMS-EEG recordings and simulations. We focus on two major types of artifacts: (1) the TMS pulse artifact during each [~]0.2 ms TMS pulse and (2) the decay artifact that may last tens of milliseconds. We examine how these artifacts change as a function of the relative position between TMS coil windings and EEG electrode leads. We also examine the hypothesis that certain EEG lead configurations may reduce or even cancel out these artifacts. In experimental results across 23 different TMS coil / EEG lead configurations, the amplitudes between the TMS pulse artifact and the decay artifact were highly correlated (Spearman {rho} = 0.86, p < 0.001), suggesting that the decay artifact is caused by the TMS pulse artifact. As predicted, in certain EEG lead configurations, both the TMS pulse and decay artifacts were minimized. The simulations confirmed that the TMS pulse artifacts depended on the electromagnetic induction from the TMS coil windings to the EEG leads. These results illuminate the generator mechanisms of--and possible means to reduce--both artifacts.

After amputation, advanced prosthetic limbs offer a promising means of restoring motor function. However, state-of-the-art prostheses often rely on aggregate electromyogram (EMG) signals to decode motor intention, which limits their ability to replicate natural limb movements. Decomposing EMG signals into individual motor unit components has shown potential for more natural control, but distinguishing between individual units can be challenging when nearby signals overlap. This study demonstrates that muscle target reinnervation surgeries can naturally increase physical separation between motor unit signals, thereby mitigating this overlap. Reinnervation of individual motor units is evaluated in a rodent hindlimb model after direct nerve-to-muscle implantation. Histological and electrophysiological analyses reveal that structural changes following reinnervation surgery result in beneficial motor unit signal changes, particularly improving spatial separation between motor unit signals compared to those in intact muscle. This spatial separation contributed to fewer instances of complex, overlapping signals in reinnervated muscle recordings. Motor unit signals were leveraged to provide a proof-of-concept of precise control of a virtual prosthesis for the first time after direct nerve-to-muscle implantation surgery. These findings highlight the potential of reinnervated muscle targets as key biological interfaces that facilitate motor unit separation, reducing the burden on decomposition algorithms and improving prosthetic control.

Restoring communication for people with dysarthria secondary to pontine stroke remains a critical challenge. Intracortical brain-computer interfaces (iBCIs) have demonstrated great potential for speech restoration in people with amyotrophic lateral sclerosis (ALS), with 1-24% word error rates (WERs) on a 125,000-word vocabulary. In pontine stroke, electrocorticography (ECoG) BCIs achieved 25.5% WERs with a smaller 1,024-word vocabulary. Whether intracortical BCI performance improvements extend to people with pontine stroke-induced dysarthria remains unclear. Here, we show that neural activity from a single 64-channel microelectrode array in orofacial motor cortex can predict attempted speech in a person with pontine stroke more accurately than prior ECoG BCI work and comparably to prior iBCI work. We trained a neural network decoder to predict phoneme probabilities from spiking rates and spike-band power as BrainGate2 participant T16 mimed (mouthed without vocalization) sentences from a large vocabulary. A series of language models converted these probabilities into word sequences. This decoding architecture has remained stable more than two years post-implantation, achieving a median 19.6% WER with a 125,000-word vocabulary and a median 10.0% WER with a 1,024-word vocabulary (a 60.8% reduction over prior ECoG studies). This framework also generalized beyond cue repetition, enabling T16 to communicate spontaneously via the iBCI in a question-and-answer setting with a 35.2% WER. These results demonstrate that brain-to-text decoding from a small patch of cortex can outperform ECoG-based systems in individuals with pontine stroke and is comparable to early speech iBCIs in individuals with ALS.

Transcutaneous spinal cord stimulation (tSCS) of the cervical spinal cord has been thought to modulate lumbar networks, leading to the hypothesis that leg muscle recruitment may occur via recruitment of long-range spinal connections between cervical and lumbar circuits. To directly test this hypothesis, we compared arm and leg muscle responses elicited in unimpaired participants (N = 12) by cervical tSCS with the anodes placed over the iliac crests, with the anodes placed over the clavicles, and with lumbar tSCS as a control for leg muscle recruitment via the posterior root-muscle reflex. The idea of tSCS targeting cervico-lumbar connectivity would suggest that cervical stimulation could evoke responses in leg muscles. However, in our experiments, leg responses via cervical tSCS were only observed when the anodes were placed over the iliac crests, but not over the clavicles. These leg muscle responses had shorter latencies than those with lumbar tSCS and showed minimal post-activation depression, indicating efferent rather than afferent recruitment. Therefore, changes in leg muscle excitability by cervical-iliac tSCS previously attributed to descending cervical circuits could instead be explained by direct recruitment of efferent fibers near the iliac anodes. These findings suggest that cervical tSCS alone does not engage leg muscle motoneurons via long-range spinal or bidirectional pathways. Therefore, our study highlights the need to carefully consider electrode configuration when interpreting cervical tSCS mechanisms and additional or unexpected rehabilitative effects that extend caudally from the cervical spinal cord.