Journal of neural engineering最新文献

Objective. Motor imagery (MI)-based brain-computer interfaces have been extensively studied. However, their widespread application is limited by the difficulty in extracting motor intentions from electroencephalography (EEG) signals, leading to low recognition rates. Additionally, the phenomenon of MI blindness in some individuals further limits its applicability. Previous studies have attempted to improve MI ability through electrical stimulation (ES). However, applying ES during MI may introduce EEG artifacts and interfere with participants' concentration. The goal of this study is to investigate a new experimental paradigm. The new experimental paradigm improves MI ability through pre-task ES while preventing participant distraction or EEG artifacts.Approach. This study implemented two paradigms: MI with pre-task ES and MI-only. Electrical stimulation was applied over hand muscle groups. Electromyography (EMG) and 64-channel EEG signals were simultaneously recorded under two experimental conditions.Main results. We analyzed cortical activities and correlations between different brain regions under the two experimental conditions. Participants in the MI-ES condition exhibited a higher level of brain activation compared to the MI-Only condition. Additionally, in the MI-ES condition, the correlation between participants' EEG and EMG signals increased after ES, indicating that the activation level of the motor-related cortex increased. A novel convolutional spiking neural network was applied to classify motor intentions, with participants achieving higher accuracy under the MI-ES condition, demonstrating enhanced MI ability through pre-task ES.Significance. This research demonstrates that pre-task ES significantly enhances MI ability, while also increasing cortical activation and corticomuscular coupling without introducing EEG artifacts or attentional interference.

Motion-induced electromagnetic interference remains a major obstacle to the accurate interpretation of surface-recorded biosignals collected during movement. This study presented a physics-based rigid-body model that integrated electromagnetic theory with a kinematic framework to describe the generation of motion-induced artifacts in surface biosignals through electromagnetic induction. The model was derived from Faraday's law and a 6D rigid-body kinematic formulation, which coupled rotational and translational motion to spatial magnetic-field gradients and curvature. This formulation predicted that any conductive loop moving within a nonuniform magnetic field produced a time-varying electromotive force (EMF) determined by the interaction between motion, field geometry, and sensor orientation. To illustrate and validate the theoretical model, computational simulations reproduced treadmill locomotion under two conditions: (1) an idealized fixed-cadence case with time-invariant field gradients, and (2) a realistic varying-cadence case incorporating stride-to-stride jitter and event-related spectral perturbation baseline correction. The simulated EMF spectra exhibited motion-locked harmonic patterns extending up to 15 Hz with electrode-dependent variations in magnitude and broadened harmonic envelopes, closely matching empirical treadmill electroencephalography spectra. Accelerometer spectra displayed broader harmonic content up to 50 Hz, consistent with their direct measurement of kinematic oscillations. Quantitative decomposition further revealed that rotational motion dominated the induced EMF, with smaller, electrode-dependent contributions from translation. Robustness analyses indicated that dominant harmonic structure is preserved under multi-axis kinematics and increased magnetic-field complexity, with greater sensitivity confined to weaker higher-order components. These results demonstrated that harmonic contamination could emerge naturally from rigid-body motion in a spatially varying magnetic field, providing a physics-based foundation for interpreting motion artifacts in surface electrical potentials and motivating practical mitigation strategies that incorporate motion and magnetic-field measurements. Through principled understanding and physics-based modeling of motion-induced electromagnetic artifacts, this framework supports interpretation of surface biosignals during movement and motivates the development of mitigation algorithms.

Objective.To establish organotypic human brain slice cultures (hBSCs) as a translational screening platform for evaluating novel neuromodulation devices and to demonstrate the feasibility of the model using magnetoelectric nanoparticles (MENPs) as a representative neurostimulation modality.Approach.Viable hBSCs were prepared from resected cortical tissue of epilepsy surgery patients and GCaMP-based calcium imaging, multi-electrode array recordings, and immunohistochemical staining for c-Fos were conducted. The MENPs were injected into the hBSCs and stimulated with an alternating magnetic field to assess their neuromodulatory effects.Main Results.GCaMP transduction enables the real-time visualization of MENP-induced neuronal activity. Electrophysiological signals, including spiking and local field potentials, were observed in fresh, but not cultured, slices. c-Fos immunostaining revealed a significant increase in c-Fos expression in stimulated MENP-injected cultures compared to sham-treated controls. This protocol yielded reproducible tissue viability and consistent results across patient-derived samples.Significance.This technical note demonstrates that hBSCs represent a reproducible and ethically preferable translational model suitable for screening applications in neurotechnology research. The platform enables early-stage functional evaluation of neuromodulatory devices, particularly those with a higher risk of failurein vivoor curiosity-driven early-phase concepts in a setting superior to traditionalin vitroapproaches. This platform may help reduce reliance on animal models in neurotechnology development.

Objective.Focused ultrasound (FUS) neuromodulation holds strong potential for treating neurological disorders, but most preclinical studies have been performed in healthy animal models. How disease states influence the FUS neuromodulation effects remains poorly understood, limiting clinical translation.Approach.We used Parkinson's disease (PD) as a model to compare the calcium and behavioral responses to FUS neuromodulation in healthy and diseased mice. The PD mouse model was the unilateral dopamine depletion model, induced by injecting 6-hydroxydopamine into the left middle forebrain bundle. FUS was targeted at the left external globus pallidus (GPe) in freely moving mice using a wearable device. Calcium activity in the GPe was monitored via fiber photometry, and motor behavior was assessed using video tracking.Main results.In unilateral PD mice, FUS significantly inhibited GPe calcium activity, and this inhibition lasted for ~3 minutes after stimulation. This inhibition was accompanied by motor improvementsas shown by a reduction in ipsilateral circling that lasted for at least 50 minutes after stimulation. In healthy mice, FUS did not significantly change the calcium activity in the GPe and rotational behavior during or after the FUS. Histological analysis revealed no evidence of neuronal damage, astrocytic activation, or microglial proliferation following the FUS.Significance.These findings demonstrate that FUS neuromodulation produces disease-state-dependent effects on calcium activity and behavior, emphasizing the importance of evaluating neuromodulation strategies in relevant disease models for clinical translation.

Objective: Conventional transcranial magnetic stimulation (TMS) coils generate a diffuse and shallow electric field (E-field) in the brain, resulting in limited spatial targeting precision (focality). Previously, we developed a methodology for designing theoretical TMS coils to achieve maximal focality for a given E-field penetration depth and minimize the required energy. This paper presents the practical design, implementation, and characterization of such focal-deep TMS (fdTMS) coils.

Approach: We considered how the coil's shape affects energy requirements and designed a curved "hat" former that enables a wide range of coil placements while improving energy efficiency compared to flat formers. To improve energy efficiency, we introduced optimized-coverage partial-multi-layer windings of the coil. Through simulations with a spherical head model, we benchmarked the focality of the fdTMS E-field in the brain and the scalp, as well as the required energy, against conventional TMS coils. We then implemented two fdTMS coil designs with copper wire wound inside a 3d-printed plastic former.

Main results: The E-field of the prototype fdTMS coils and conventional figure-8 counterparts were simulated in spherical and realistic head models and measured with a robotic probe, confirming a more compact fdTMS E-field. The fdTMS coils were also compared to two commercial coils with motor mapping in nine human subjects, which confirmed improved focality of fdTMS at the cost of greater E-field spread, increased energy loss and heating from the smaller wire diameter positioning constraints of the curved coil surface.

Significance: The study findings inform TMS coil implementation for precise mapping and targeting applications, and the design framework can be leveraged for future coil optimizations.

Objectives: Neuron morphology plays a vital role in defining cellular identity and function, offering valuable insights for cell type classification and neurological disorder diagnosis. However, two main challenges hinder progress: the difficulty of learning meaningful representations from complex, tree-like structures, and the high cost of expert annotation for large-scale datasets.

Approach: To address these challenges, we propose MorphSys, a self-supervised contrastive learning framework that complements a Branch-Aware module and a GNN-based module. We present a branch-level representation of neuron morphology by introducing an Inter-Branch Attention, which captures inter-branch relationships that are overlooked by conventional tree-graph models relying on node-level message passing. We further demonstrate the effectiveness and interpretability of branch-level knowledge in learning meaningful representations of neuron morphology. Meanwhile, our GNN-based module shows a robust ability for various GNN architectures in learning local features of neuron tree graph, where we draw from results that GatedGraphConv with SumPool yields the superior performance.

Main results: Comprehensive experiments on multiple benchmark datasets indicate that MorphSys consistently outperforms existing methods in neuron cell type classification. On the BIL dataset, MorphSys achieves the KNN-Acc of 83.99%, surpassing the previous state-of-the-art by 2.99%. Ablation study is conducted to verify the efficacy of several components of MorphSys, while an in-depth discussion is performed to identify powerful approaches for branch feature extraction.

Significance: These results highlight that MorphSys serves an effective tool for the representation learning of neuron morphology and morphology-driven neuronal analysis.

Objective.Accurate detection of single-trial P300 ERPs (event-related potentials) is crucial for developing high-performance non-invasive BCIs (brain-computer interfaces). However, this task remains challenging because of the low (signal-to-noise ratio) of EEG (electroencephalography) and the limited ability of existing models to concurrently capture the complex non-Euclidean spatiotemporal dynamics of brain signals.Approach.We propose a novel ST-GraphTRNet (spatiotemporal graph transformer network). This architecture synergistically integrates temporal convolutions for local feature extraction, graph convolutions to explicitly model the neurophysiological spatial relationships between EEG electrodes, and a temporal transformer with a self-attention mechanism to capture global, long-range temporal dependencies across the entire signal.Main results.Extensive evaluation of four public P300 datasets demonstrates that ST-GraphTRNet significantly outperforms (state-of-the-art) benchmarks under both within-subject and cross-subject paradigms. Crucially, interpretability analyzes via (T-distributed Stochastic neighbor embedding) and (Gradient-weighted Class Activation Mapping) revealed that the model's decisions aligned with established neurophysiological priors, focusing on parietal electrodes approximately 300 ms post-stimulus.Significance.This study provides a powerful and interpretable framework for single-trial ERPs decoding. By effectively integrating the strengths of (convolutional neural networks), (graph neural networks), and Transformers, a new benchmark for building high-accuracy, generalizable, and clinically viable BCIs is established, moving closer to the goal of plug-and-play systems that require minimal user-specific calibration.

Objective.Upper limb amputation severely limits daily activities and independence. Current prosthetic control methods often rely on surface electromyography (sEMG), which suffers from low signal quality and limited functionality. This study investigates whether implanted electrodes in regenerative peripheral nerve interfaces (RPNIs) and residual innervated muscles can provide stable and high-quality control signals to improve dexterous prosthetic hand and wrist function.Approach.Two individuals with upper-limb amputation had RPNIs created by suturing free skeletal muscle grafts to peripheral nerves or nerve fascicles in the residual limb. Intramuscular EMG (iEMG) electrodes were implanted into the RPNIs and muscles in the residual limb. EMG signals were recorded from both sEMG and iEMG electrodes and used to control a virtual prosthetic hand + wrist in real time. Performance was assessed through multiple degrees-of-freedom (DoF) control tasks, comparing RPNIs and iEMG against conventional sEMG.Main Results.Implanted electrodes demonstrated high signal-to-noise ratios and long-term stability, enabling independent and simultaneous control of multiple hand + wrist DoFs. Participants achieved faster, more accurate, and more reliable control using RPNIs and iEMG-based control compared with sEMG-based systems, based on classification accuracy and trial success rate. Importantly, we find that the ability to control wrist rotation reduces total body compensations when performing a functional assessment (Coffee Task), and implanted electrodes greatly reduced task completion times compared to surface electrodes when wrist rotation was added as an additional control movement.Significance.In this study, we demonstrate that RPNIs and iEMG electrodes in combination enable significantly more accurate and stable prosthetic control of hand and wrist movements compared to surface electrodes, especially during dynamic arm movements. These findings suggest that RPNIs and iEMG electrodes offer meaningful advantages over sEMG for achieving more intuitive and reliable control of upper-limb prostheses in real-world conditions.

Objective: To evaluate the influence of head morphology on the performance of a wearable setup that incorporates the constraints of an eyewear-EEG device suitable for consumer-level applications. Specifically, the study aimed to characterize the electrode-skin impedance of two dry-electrode types mounted on eyeglass frames, assess the system's ability to capture alpha-rhythm modulation during eyes-open and eyes-closed (EOEC) states in the temporal region, and its capability to detect auditory event-related potentials (P300).

Approach: A prototype was built by embedding four EEG electrodes, two gold-plated retractile pins (GPR) and two conductive elastomer (CoE), into a commercial eyeglass frame, with reference and bias on the nose pads. Signals were acquired using an OpenBCI Cyton board (ADS1299 analog front end, sampling at 256 Hz). Twenty young healthy adults underwent three experimental protocols, namely electrode-skin contact assessment, eyes-open/eyes-closed tasks (two cycles of 2 minutes each) to examine alpha-band (8-12 Hz) power changes and compute an alpha-to-broadband power ratio, and an auditory oddball paradigm (80% standard, 20% odd stimuli, 50 odd trials) to elicit and analyze P300 components.

Main results: GPR electrodes exhibited moderately higher median impedance but slightly narrower confidence intervals compared to CoE electrodes. Head breadth significantly affected GPR impedance (≈ 11.7% decrease per mm increase), but had no significant effect on CoE impedance. Alpha-band power increased significantly during eyes-closed periods across subjects and electrode types. P300 responses (positive deflection at 300 ms) were reliably detected, with GPR electrodes yielding tighter latency distributions.

Significance: These findings emphasize the importance of careful design considerations in wearable-EEG to account for inter-subject head anatomy variability and demonstrate that eyeglass-integrated EEG, can reliably capture both evoked and spontaneous neural responses.

Objective.Visual impairments create significant challenges for navigation. This work explored the potential for an autonomous navigation aid with multisensory feedback to improve navigational performance for users of visual neuroprostheses.Approach.An autonomous navigation system was developed that maps the environment in real time and provides guidance using combinations of prosthetic vision, haptic, and auditory cues. Navigational performance was evaluated in 20 sighted participants using simulated prosthetic vision and in a single-subject case study of an Argus II visual neuroprosthesis user. Participants completed three tasks: navigate to destination, obstacle field traversal, and relative distance judgment. Multiple sensory feedback configurations incorporating visual, haptic, and auditory cues were compared. Performance metrics included collision rate, distance traveled, task completion time, navigation success rate, and accuracy of relative distance judgments.Main results.Performance differences across sensory configurations were most pronounced in navigation success and collision rates. Haptic plus audio feedback was highly effective for navigation tasks, enabling successful navigation in nearly all trials involving haptic guidance. Argus vision (AV) alone was inadequate for navigation. Depth vision (DV) provided modest improvements over AV but did not enhance performance beyond haptic and audio guidance when combined. Wide field-of-view DV yielded additional benefits, particularly for obstacle field traversal where its performance exceeded other modes. Adding AV to haptic and audio also provided no benefit and, in some cases, degraded performance. Performance trends for the Argus user were generally comparable to those of sighted participants across sensory modes, with the exception of the relative distance judgment task, in which the Argus user demonstrated better performance. Among sighted participants, increased field of view and resolution independently improved relative distance judgment accuracy.Significance.These findings demonstrate the potential of multimodal feedback systems to improve navigation for prosthetic vision users. (ClinicalTrials.gov NCT04359108).