Journal of Neural Engineering

Brain-machine interfaces (BMIs) for vision restoration require models that accurately simulate the anatomy and electrical properties of visual pathways. However, current models focus only on isolated structures, such as the retina or brain, and overlook surrounding tissues. Here, we present a comprehensive computational model of the human head, incorporating the entire visual pathway--including the eye, optic nerve, and brain--along with critical neighboring tissues such as the orbit, paranasal sinuses, enabling precise simulations. Validation using human and large animal data demonstrated a strong correlation between the simulated and measured electrical potentials. Component elimination analysis revealed that the optimized comprehensive model outperformed simplified versions. The models utility was demonstrated through multiple applications: (1) comparative analysis of electrical neuromodulation technologies for optic neuropathy, revealing the filed intensity limitations of noninvasive approaches and the safety concerns of invasive intraorbital approach; (2) identification of optimal stimulation site, revealing that transnasal stimulation at the optic chiasm outperformed traditional approaches; and (3) in silico design of electrode arrays for optic nerve prosthetics, demonstrating theoretical advantages in invasiveness and visual field coverage compared to existing retinal and cortical prosthetics. This validated and versatile computational resource supports the development of neuromodulation strategies and visual BMI technologies.

BackgroundPeripheral nerve stimulation (PNS) is most effective when specific nerve fiber subpopulations are activated, while minimizing off-target activation, which may cause undesirable side effects. This selectivity depends primarily on electrode design and charge delivery. We hypothesized that selective PNS could be achieved through electrode placement and intrafascicular electric field steering using Longitudinal Intrafascicular Electrodes (LIFEs). MethodsLIFEs were implanted into the tibial fascicle of the sciatic nerve of 17 anesthetized adult rats. We tested whether electrodes positioned at different cross-sectional and longitudinal locations within the same fascicle, together with different electric field-steering approaches produced distinct activation patterns in the gastrocnemius lateralis muscle. Muscle responses were measured using high-density epimysial electromyography (HD-eEMG). ResultsElectrodes placed at different locations within the same fascicle activated distinct muscle regions, demonstrating intrafascicular selectivity. Bipolar stimulation recruited nerve fibers differently than monopolar stimulation, showing that electric field steering can further shape the selective recruitment. In both configurations, increasing the stimulation amplitude produced a graded increase in muscle activation. Furthermore, our findings demonstrated that HD-eEMG is an effective tool for evaluating intrafascicular selectivity. ConclusionThese findings suggest that improving on-target selectivity may support next-generation bioelectronic therapies with better outcomes and fewer side effects, potentially enabling more precise, organ-specific neuromodulation. Using multiple intrafascicular electrodes may provide two complementary strategies for enhancing selectivity: strategic intrafascicular placement to access different fiber subpopulations and bipolar configurations to steer recruitment beyond what a single electrode can achieve.

Reconstructing speech envelopes from electroen-cephalography (EEG) signals is a challenging but valuable task for brain-computer interfaces (BCIs), with applications in assistive communication for individuals with speech impairments. While deep learning has improved reconstruction accuracy, most existing approaches are restricted to single-layer architectures such as convolutional neural networks (CNNs). This limits their ability to capture the full complexity of spatio-temporal and structural EEG patterns. In this work, we systematically extend the VLAAI framework by evaluating 26 architectures that integrate CNNs, long short-term memory networks (LSTMs), and graph convolutional networks (GCNs) in both single-layer and hybrid configurations. Experiments on the 64-channel Spar-rKULee dataset demonstrate that CNNs remain the strongest standalone models, but hybrid designs--particularly CNN-LSTM and CNN-GCN-LSTM--achieve competitive or superior performance. These results highlight the importance of combining spatial, temporal, and graph-based processing, and provide practical guidelines for hybrid architecture design. Our study offers the first large-scale comparative analysis of hybrid models for EEG-based speech envelope reconstruction, advancing robust BCI systems for non-invasive speech decoding.

BackgroundEar-EEG-based brain-computer interfaces (BCIs) provide improved wearability and comfort compared to traditional scalp-EEG systems. However, their performance is constrained by low signal-to-noise ratios (SNRs) and high rates of BCI illiteracy under conventional luminance-modulated steady-state visual evoked potential (SSVEP) paradigms. MethodsThis study introduces a text-sequence stimulation paradigm to address these limitations by leveraging ventral visual pathway responses that are more accessible to electrodes near the ear. Using offline frequency-sweeping experiments across 4-8 Hz, we identified optimal stimulus parameters (4.6-6.8 Hz with 0.25{pi} phase shifts) and integrated them into a 12-target BCI system. We further conducted online experiments to compare the response characteristics and real-time spelling performance between the proposed text-sequence paradigm and conventional luminance stimulation. ResultsComparative experiments with 14 participants demonstrate that text sequence stimuli achieve an average information transfer rate (ITR) of 44.59 {+/-} 10.50 bits/min, outperforming luminance modulation by 76.18% in ITR. Notably, text sequence stimulation effectively mitigated BCI illiteracy, with all participants achieving near or above 70% accuracy (mean: 86.37 {+/-} 9.61%). This represents a significant improvement over luminance modulation, where 50% of users fell below 70% accuracy. ConclusionsBy reducing the flicker area by 14% and mimicking the natural luminance variations that occur during reading, the proposed method enhanced visual comfort. The online results further validate text-sequence stimulation as a high-performance and user-friendly paradigm for ear-EEG BCIs, supporting their practicality for assistive applications.

Machine-learning surrogate models are positioned to help optimize deep brain stimulation (DBS) usage by predicting neural activation in response to electrical stimulation, while minimizing tradeoffs between computational expense and accuracy. Previous work has developed high accuracy artificial neural network (ANN) and convolutional neural network (CNN) surrogate models that predict activation of individual, myelinated axons, to extracellular electrical stimulation for subsets of DBS programming configurations. Moreover, more traditional machine learning methods including extreme gradient boosting (XGBoost) have been used effectively for peripheral-nerve single-fiber activation predictions. We build upon the previous work and compare ANN, CNN and XGBoost methods to a much expanded set of electrode programming configurations including: monopolar, bipolar, tripolar, quadrupolar, multiple monopolar, and multiple cases of directional leads. Training used datasets generated from a finite-element model of an implanted DBS lead together with multi-compartment cable models of synthetically generated axons. We evaluated the machine learning predictors using white matter pathways derived from group-averaged connectome data within a patient-specific tissue conductivity field, comparing both predicted stimulus activation thresholds and pathway recruitment across a clinically relevant range of stimulus amplitudes and pulse widths. Our ANN and CNN models successfully predicted neural fiber activation for almost all electrode configurations with low error, expanding the scope of our previous predictor model. Results also showed key limitations of XGBoost models and superior performance of CNNs for more complex electrostatic fields of the directional leads.

Transcranial temporal interference stimulation (tTIS) is a promising non-invasive brain stimulation technique that has the potential to selectively modulate deep brain regions by delivering two high-frequency alternating currents that interfere to produce a low-frequency amplitude-modulated envelope at the target. A key challenge in deploying tTIS is finding electrode current patterns that are simultaneously effective, focal, and safe. This is a fundamentally non-convex optimization problem for which a number of methods have recently been proposed. However, no systematic comparison of these methods across a large and diverse set of brain targets has been performed, leaving practitioners without clear guidance on how best to optimize for a particular experimental setting. In this work, we present a comprehensive benchmarking study comparing seven tTIS optimization methods that have appeared in the literature in recent years: exhaustive search, genetic algorithm, multi-objective evolutionary algorithm (MOVEA), min-max optimization, convex TI (CVXTI), non-convex optimization with convex relaxations, and an unsupervised neural network. All methods were evaluated across 250 brain targets spanning cortical and subcortical gray matter and white matter regions in five finite element head models. Each method was evaluated on two key metrics: mean electric field strength within the target region of interest, and off-target stimulated brain volume. Results were stratified by tissue type and target depth to identify systematic performance differences. Based on these results, we provide practical evidence-based recommendations for optimization method selection among these seven methods depending on target location, tissue type, and available computation time. Moreover we provide the code base that will allow other investigators to use these methods for their own applications. Our goal is to provide researchers and clinicians with a clear, evidence-based framework for choosing a tTIS optimization method suited to their specific target and application.

Kilosort 2.0 is a widely adopted spike sorting algorithm recognized for its efficiency and accuracy on planar electrode arrays, such as Neuropixels. To adapt its robust architecture to emerging three-dimensional (3D) neural probes, we present Kilosort 2.0-3D, a modified version that leverages 3D spatial information. Our modification specifically redefines the spatial processing components of Kilosort 2.0 to operate in 3D space while leaving the core template-matching process unchanged. By using synthetic extracellular recording data with ground-truth neuron positions and firing times, we demonstrate that Kilosort 2.0-3D effectively resolves spatial ambiguities and unit misclassifications inherent in 2D spatial assumptions. Our results show that Kilosort 2.0-3D achieves rotational invariance and maintains full backward compatibility with planar arrays. This work establishes a validated, scalable tool for spike sorting of high-density 3D neural electrophysiology data.

IntroductionVagus nerve stimulation modulates laryngeal, cardiac, pulmonary, and gastrointestinal functions. Knowledge of where along the vagal trunk organ-specific branches emerge may support alternative surgical placements of stimulation devices to engage targeted functions while avoiding off-target effects. However, no quantified map of how vagal branches emerge and how they relate to surgically relevant anatomical landmarks exists in humans. MethodsFifty-eight vagus nerves (29 left, 29 right) from 29 embalmed donor bodies (15 females) were dissected from the jugular foramen through the thoracic cavity. Branches were traced to end organs and allocated to seven groups -- sympathetic, muscular, vascular, cardiac, pulmonary, esophageal, and multiple targets -- and several sub-groups. Distances between branch emergence and the jugular foramen (JF) were normalized to three anatomical landmarks: carotid bifurcation, laryngeal prominence, and superior border of clavicle. ResultsBranch emergence follows a proximal-to-distal order: sympathetic (5.28 cm from JF), muscular (9.59 cm), vascular (10.70 cm), cardiac (19.65 cm), pulmonary (25.36 cm), and esophageal (26.57 cm). Vagal branches emerge into two embryological domains separated near the clavicle: pharyngeal arch-targeting branches cluster proximally (9.34 cm) and primitive mediastinum-targeting branches cluster distally (23.74 cm), with sympathetic, muscular, and vascular sub-groups occupying distinct zones within the proximal domain. The largest branch-free intervals occur above the left clavicle (2.33 {+/-} 2.80 cm) and below the left carotid bifurcation (2.58 {+/-} 3.17 cm). Alternate placement regions separating targeted organs from off-targets: sympathetic vs. cervical visceral at 6/8 cm (L/R), cardiac vs. carotid sinus/bifurcation at 14/10 cm, and recurrent laryngeal vs. other cervical visceral at 18/13 cm from JF. Overall, no differences were found between male and female donors. ConclusionsThis study provides a quantified, landmark-registered map of cervical and thoracic vagal branch emergence, offering a standardized anatomical template that may inform strategies for more function-selective vagal neuromodulation.

BackgroundElectrophysiological recordings from chronically implanted Deep Brain Stimulation (DBS) electrodes can greatly advance understanding of disease and treatment mechanisms of motor and psychiatric disorders. The Medtronic Percept system allows for chronic recordings of local field potentials (LFP) from DBS target regions. However, these systems lack an inbuilt synchronization option to align LFP recordings to other recording modalities and consequently events in computerized tasks. ObjectiveWe propose and evaluate a synchronization method based on Transcutaneous Electrical Stimulation (TES) with low amplitudes to precisely align recorded LFP signals from the DBS electrodes to EEG recordings. MethodsThe TES-based synchronization approach was implemented and tested in 11 participants implanted with the Medtronic Percept for treatment of Parkinsons disease. ResultsThe proposed method provides high reliability, precise alignment and usability across all Medtronic Percept recording modes. Notably, the method enables recordings during adaptive DBS and with stimulation turned off. In this recording mode, LFP signals can be acquired from all recording contact pairs simultaneously, with a high signal-to-noise ratio. We provide detailed setup plans and share Python and Matlab scripts for signal alignment to enable easy application of our approach. ConclusionBy enabling reliable, well-aligned LFP recordings from all DBS contacts, our method provides a robust tool for studying neural dynamics and refining therapeutic interventions in diverse neurological conditions.

Visual prostheses face a critical miniaturisation challenge: converting photoreceptor signals to biologically appropriate retinal ganglion cell (RGC) stimulation patterns within the spatial constraints of intraocular implants. Existing systems rely on external microcontrollers for signal processing, limiting scalability for high-density pixel arrays. This paper presents an integrated per-pixel circuit architecture that directly converts photocurrent into frequency-modulated current pulses that match RGC activation thresholds. The design targets are established through NEURON computational modelling of red-green colour-opponent midget RGCs, identifying stimulation thresholds of +0.1nA to +3.5nA for depolarisation and -0.1nA for repolarisation. The proposed circuit combines a transimpedance amplifier, a voltage-controlled oscillator with a Schmitt trigger, and a current-controlled output stage to generate biphasic pulses within these thresholds. A complementary output provides lateral inhibition, reducing crosstalk between adjacent RGC stimulation sites. Photoreceptor integration is achieved using P3HT:PCBM organic photodiodes for cone-associated RGCs and phototransistors for rod-associated RGCs, validated through OghmaNano finite element simulations. The photodiode circuit produces output frequencies of 2.5Hz (dark) to 600Hz (100 W/m2), matching reported RGC response ranges. This architecture eliminates external processing requirements, enabling scalable high-density retinal prostheses design.

Extracting stable subject-specific features from EEG signals remains challenging due to their entanglement with transient brain states. We propose a universal neural framework that disentangles subject-specific features from state-dependent components in raw EEG signals. Our approach employs a disentanglement module with a cross-reconstruction objective to isolate subject-specific representations. We validate our framework on EEG-based biometric recognition using two public datasets with leave-one-state-out cross-validation. Results demonstrate significant improvements in out-of-distribution identification accuracy across four different backbone models, confirming our methods universality and plug-and-play capability. This work advances reliable extraction of neural signatures for personalized neurotechnology applications.

The most prominent signature associated with motor execution and motor imagery is the event-related desynchronisation and synchronisation (ERD/S) in the mu and beta bands (8-30 Hz). In the context of brain-computer interfaces (BCI), this ERD/S signature is helpful for binary decisions, such as left vs. right imagery, but it is not a robust biomarker for continuous prediction, such as precisely decoding different levels of force application. This is essential for developing better BCI applications with precise dynamic force outputs. Recent studies have revealed that sensorimotor beta bursts have a stronger relationship with motor control, even at a single-trial level, than beta band power. We, therefore, investigated whether the transient nature of beta bursts provide an alternative, but robust biomarker for BCI force decoding. Here, we designed an experiment where human participants (N = 16) performed both motor execution (ME) at four force levels (10%, 25%, 50%, and 75% of maximum voluntary contraction) and imagined exerting the same, i.e. a motor imagery (MI) task, as their electroencephalogram was recorded. We observed a clear and classical ERD pattern in the motor cortex during the ME task, whereas it was less pronounced during the MI task. After extracting sensorimotor beta bursts, we observed differences in spectral burst features between motor execution and imagery including burst amplitude, spectral width, and temporal width. Moreover, different force levels were correlated with changes in the burst amplitude and burst spectral width, specifically during motor execution. Interestingly, we found that different beta burst waveforms are associated with the different force levels and conditions. This suggests that the bursts-level features could be driven by changes in the underlying beta burst waveforms. Overall, our study shows that sensorimotor beta burst can be an important piece of the puzzle to implementing precise force control in brain-computer interface-based prosthetics.

When hearing fails, stimulation of the auditory nerve by electrical cochlear implants (eCIs) partially restores hearing, with most eCI users achieving open speech understanding. However, the broad current spread from each electrode limits frequency coding and speech understanding in daily situations with background noise. Spatially confined optogenetic stimulation by future optical cochlear implants (oCIs) improves frequency coding but millisecond closing kinetics of channelrhodopsins (ChRs) might limit temporal coding. Here, we evaluated the utility of fast-closing ChR f-Chrimson for processing temporal information in the auditory system of Mongolian gerbils. We recorded neural activity in the inferior colliculus evoked by f-Chrimson-mediated optogenetic stimulation of the cochlea. F-Chrimson enabled energy-efficient stimulation of the auditory pathway at rates [≥]150 Hz, outperforming the slower ChR variants CatCh (blue) and ChReef (green). Energy thresholds for activation of the auditory pathway were in the low {micro}J range, between ChReef (sub-{micro}J) and CatCh. Dynamic range and frequency selectivity were comparable to previous observations with CatCh and outperformed electrical stimulation. In conclusion, employing fast-gating ChRs harnesses improved spectral coding without degrading temporal coding. The Paper ExplainedO_ST_ABSProblemC_ST_ABSElectrical cochlear implants (eCIs) partially restore speech comprehension in most of 1 million otherwise severely deaf people. However, most CI-users face challenges hearing in daily situations. Spectrally more selective stimulation of the auditory nerve by optical cochlear implants (oCIs) promises to overcome this limitation. However, the closing kinetics of channelrhodopsins (ChR) limit the temporal bandwidth of bionic sound coding. Improving the ChR properties and evaluating temporal coding remain major objectives for developing hearing restoration by oCI. ResultsHere, we evaluate the utility of waveguide-based oCI using the fast-closing ChR Chrimson (f-Chrimson) for encoding of temporal, spectral and intensity information by multi-electrode-array (MEA) recordings from the midbrain. We compare f-Chrimson-mediated bionic coding to acoustic coding as well as to previous data acquired with optogenetic stimulation using other ChRs and with electrical stimulation. F-Chrimson enabled energy-efficient stimulation of the auditory pathway at rates [≥]150 Hz, outperforming the slower ChR variants CatCh (blue) and ChReef (green). Intensity and frequency coding were comparable to previous observations with CatCh and outperformed electrical stimulation. ImpactThis study demonstrates near physiological temporal coding with the fast-closing ChR f-Chrimson, indicating that improved spectral coding by oCI is not traded off by poor temporal fidelity.

Closed-loop deep brain stimulation (cDBS) for Parkinsons disease requires control strategies that tolerate noisy sensing, patient-specific stimulation responses, medication-related fluctuations, and embedded hardware constraints. We developed a patient-calibrated minute-scale dynamical model of subthalamic beta activity and an embedded explicit trend-zone predictive controller, eTZPC. The model combined a basal-ganglia mechanistic prior with stimulation-amplitude and medication-cycle recordings from five patients, and incorporated individualized stimulation-{beta}STN maps, fast- and slow-timescale stimulation responses, levodopa-related modulation, background drift, and observation noise. eTZPC was designed to maintain {beta}STN activity within a patient-specific target zone under stimulation-amplitude, step-size, and quantization constraints. Compared with dual-threshold (DT) and proportional-integral-derivative (PID) controllers across four disturbance scenarios, eTZPC achieved target-zone regulation close to PID while reducing stimulation-switching burden toward the low-switching profile of DT. Ablation analyses identified distinct contributions of smoothing, trend prediction, patient-specific action modeling, and embedded explicit implementation. Parameter-mismatch tests showed that eTZPC was relatively robust to dynamic and disturbance-parameter deviations, but remained sensitive to errors in the steady-state stimulation-{beta}STN map. Patient-in-the-loop recordings in five patients further confirmed execution consistency and compliance with stimulation-boundary and step-size constraints. These findings support patient-calibrated dynamical modeling combined with low-complexity explicit control as a feasible framework for further embedded cDBS evaluation.

Brain-state-guided and closed-loop transcranial magnetic stimulation (TMS) protocols have emerged as methods for decreasing the variability and increasing the therapeutic effectiveness of stimulation protocols. However, most existing brain-state-dependent TMS systems only control the timing of stimulation, while the location is fixed and manually adjusted between blocks or sessions. This limits flexible targeting of distributed networks. We developed a system that jointly manages TMS pulse timing and location automatically controlled by an electroencephalography (EEG)-based brain- computer interface (BCI). A machine-learning algorithm infers the brain state in real time to guide the robotic coil placement and target. We present a proof-of-concept study in which a BCI controlled both the target site and the timing of TMS. A pre-trained convolutional neural network discriminated between resting state and movements performed with the right or left hand; the classifier output determined the hemisphere in which primary-motor-cortex hand area was stimulated and when. Preprocessing and decoding of 2-s EEG segments required 150 ms, and the robot took 7.5 s to move from the vertex home position to the predefined motor targets. The EEG-BCI-guided robotic TMS system expands the toolkit for brain-state-dependent and closed-loop neurostimulation by enabling control of stimulus location based on volitional brain activity. Thus, the system can benefit both neuroscience research and clinical neuromodulation applications. A prominent application of the system is automatically controlling spinal cord injury or motor disorder TMS rehabilitation with motor imagery, optimizing stimulation timing to the brain state producing optimal rehabilitation results.

Neural oscillatory phase is widely used as a control variable in real-time closed-loop stimulation, yet its validity under strict causal constraints and noisy conditions has rarely been systematically examined. We introduce a Multidimensional Gating Framework (MGF), a plug-in and estimator-agnostic module that determines whether phase information should be admitted into control by evaluating instantaneous amplitude, narrowband signal-to-noise ratio (SNR), and spectral peak ratio (PR) within a strictly causal window. Using causal streaming replay on a public resting-state EEG dataset, we benchmarked Hilbert based phase estimation and endpoint-corrected Hilbert estimation with and without MGF. Among feasible subjects, MGF significantly reduced phase dispersion for both estimators, while robustly suppressing catastrophic phase errors. In contrast, ungated approaches exhibited systematic failures under the same conditions.

ObjectiveNeurogenic lower urinary tract dysfunction (NLUTD) impairs bladder control and remains difficult to treat. We aim to define how electrical stimulation (ES) parameters of the external urethral sphincter (EUS) affect urinary leakage thresholds to guide neuromodulation strategies for NLUTD. MethodsWe performed direct EUS stimulation in anesthetized rats using charge-balanced biphasic pulses while systematically varying current amplitude (0.5-3.0 mA), frequency (20-100 Hz), and pulse duration (0.5-3 ms). Urine leakage thresholds were mapped across the multidimensional parameter space. ResultsStimulation parameters exhibited strong nonlinear interdependence in determining leakage onset. At a fixed pulse duration, higher current amplitudes required lower stimulation frequencies to evoke leakage. Increasing pulse duration substantially reduced both current and frequency thresholds. Age and sex caused modest shifts in absolute thresholds but did not alter the fundamental parameter-response relationships. ConclusionPulse duration, current amplitude, and frequency jointly govern urinary leakage thresholds, with pulse duration serving as the dominant modulator of stimulation efficiency. SignificanceThis work establishes a quantitative framework for charge-efficient stimulation parameter selection, enabling the design of energy-aware, precision neuromodulation protocols and implantable systems for NLUTD rehabilitation.

When hearing fails, cochlear implants (CIs) partially restore auditory perception. Yet, poor coding of spectral information remains a bottleneck as each electrode broadly activates the auditory nerve. As light can be more conveniently confined, optical (o)CIs present a promising alternative. Here, we combined expression of the potent channelrhodopsin ChReef in spiral ganglion neurons (SGNs) and oCIs based on 5-10 green LED in gerbils. We characterized the oCI encoding of intensity and spectral information by ChReef-SGNs using recordings from the central nucleus of the inferior colliculus (ICC). ChReef aligned light sensitivity of SGNs well with the radiant fluxes provided by individual LEDs: ICC-activity had thresholds <200 nJ and reached a maximum close to that achieved with 46 dB tones. Multichannel oCIs enabled tonotopically ordered and spectrally distinct stimulation indistinguishable from acoustic stimulation for up to moderate activity levels. Some LEDs elicited >1 spectral peaks for stronger intensities. Representational Similarity Analysis and Linear Discriminant Analysis of ICC activity indicated improved channel discriminability of optical over electrical stimulation. In summary, {micro}J oCI stimulation achieves near-physiological spectral resolution. The Paper ExplainedO_ST_ABSProblemC_ST_ABSElectrical cochlear implants (eCIs) partially restore speech comprehension in most of >1 million otherwise deaf users, who still face challenges hearing in daily situations. This is primarily due to poor spectral selectivity of electrical sound encoding. Spatially more confined optogenetic activation of the auditory nerve by optical cochlear implants (oCI) promises to overcome this limitation. However, a thorough characterization of bionic coding of sound information by multichannel oCI is needed to evaluate the potential for improved hearing restoration. ResultsHere, we combine the potent channelrhodopsin ChReef and 10-channel oCI based on green LEDs in gerbils and characterize their utility for encoding of spectral and intensity information by multielectrode array recordings from the midbrain. ChReef enabled activation of the auditory pathway with nano-joule thresholds and up to high levels of midbrain activity with low {micro}J radiant energy. The cochlear spread of excitation and channel discriminability for low to medium activity levels were close to what we observed with acoustic stimulation. ImpactOur work demonstrates great potential of multichannel optogenetic stimulation for encoding sound frequency information.

High-density electrophysiological recording using Neuropixels probes enables single-unit resolution of human neural activity. However, integrating these systems into clinical environments remains challenging. Reported human recordings have been limited to a few centres in the United States utilising variable regulatory, sterilisation and operative techniques. Here, we present human Neuropixels recordings under a nationally managed ethical and regulatory framework in the United Kingdom. We provide a reproducible roadmap to overcome regulatory and equipment constraints. Guided by the IDEAL Stage 2a (Development) framework, we established a frameless intraoperative workflow utilising manufacturer-sterilised probes and a commercially available, clinical-grade setup for Neuropixels insertion including micromanipulator and endoscope holder. We prospectively evaluated this workflow across six participants (mean age 62.5 years) undergoing elective ventriculoperitoneal shunt surgery. Iterative failure-mitigation cycles successfully resolved key technical barriers, including neuronavigation interference and hardware instability. Assessed across three predefined endpoints (clinical safety, procedural timing, and neural data yield), the workflow achieved zero research-related adverse events and maintained a strict 30-minute procedural extension. Progressive technical refinements increased single-unit yield from 25 units during early development to 146 manually curated units. This approach provides a scalable, clinically integrated workflow to safely perform high-density electrophysiology in routine neurosurgical environments.

ObjectiveInvestigation of the feasibility and characteristics of contactless motor unit number estimation (MUNE) using optically pumped magnetometer-based magnetomyography (OPM-MMG) as compared to surface electromyography (EMG). MethodsSimultaneous electrically evoked OPM-MMG and EMG signals of the abductor digiti minimi muscle (ADM) were measured in three healthy participants. To characterize MUNE across both modalities and account for within-subject physiological variability, 20 repetitions of electrical stimulation of the ulnar nerve at randomized intensities ranging from 5 to 30 mA in 0.1 mA increments were performed, resulting in a total of 5,020 evoked responses per subject. We quantitatively compared of MUNE and evoked EMG/MMG signal characteristics, including signal-to-noise ratio (SNR) and motor unit response amplitudes. SNR was equalized between measurement modalities to estimate the effect of SNR on MUNE. ResultsMMG-derived MUNE (mMUNE) could be assessed contactlessly. mMUNE estimates were on average 40% lower than EMG-derived MUNE (eMUNE), ranging from 30-65 for mMUNE versus 69-101 for eMUNE. Equalizing the EMG SNR (29-31 dB) to match the MMG SNR (18-27 dB) yielded comparable eMUNE and mMUNE estimates. Peak-to-peak amplitudes of the supramaximal compound motor unit fields ranged from 34-73 pT and single motor unit fields ranged from 0.7-1.4 pT. SignificanceThese findings demonstrate that OPM-MMG enables contactless motor unit number estimation.