Journal of neural engineering最新文献

Human patients with somatosensory loss often experience severe motor deficits, causing profound challenges to independently accomplish typical tasks of daily life. Brain-machine Interfaces (BMIs) offer the potential to restore lost functionality through direct electrical stimulation of the somatosensory cortex via intra-cortical micro-stimulation (ICMS). By modulating temporal patterns of stimulation, our group has previously shown single-channel ICMS can evoke both naturalistic cutaneous and proprioceptive sensory feedback. However, accurate modulation of the sensory feedback's qualia (somatotopic location, intensity and description) will be critical for fluid, dexterous motor control. In nonhuman primate studies, multi-channel ICMS has shown promise in improving quantifiable metrics such as reaction time. In recent human work, multi-channel ICMS has improved discrimination performance; however, evoked qualia from multi-channel ICMS has not been well characterized. We hypothesized multi-channel ICMS could evoke unique qualia compared to single-channel. A human participant with tetraplegia and chronically implanted microelectrode arrays in primary somatosensory cortex, reported perceptual thresholds, sensation descriptions, intensity and somatotopic locations of single- and multi-channel ICMS patterns. We found multi-channel ICMS patterns evoked unique qualia compared to single-channel ICMS. To investigate the role of charge in producing these unique evoked sensory percepts, we delivered equal amounts of charge with differing spatial patterns across multiple electrodes. Multi-channel ICMS substantially reduced the minimum stimulation amplitude required to evoked somatosensations, lowering the charge per electrode detection threshold, while increasing the total charge injected. Delivered charge across multiple electrodes, positively modulated the sensation's perceived intensity; providing early evidence of spatial integration of ICMS in the target network. Multi-channel ICMS resulted in more frequent verbal reports of 'natural' sensation descriptors (100% vs 85% for single-channel ICMS,p-val < 0.05) and robustly evoked sensations with high repeatability in stable somatotopic locations. Multi-channel ICMS patterns demonstrated improvements in reliability, somatotopic coverage and 'natural-ness' of the evoked sensations, marking significant advances towards state-of-the-art somatosensory BMIs. By better understanding of the input/output relationship for somatosensory feedback BMIs, we can expect to improve movement accuracy and increase embodiment for human users.

Brain-computer interface (BCI) technology enables direct communication between the human brain and external devices by decoding electroencephalography (EEG)signals into actionable commands. As a noninvasive and portable modality, EEG-based BCIs hold promise for applications ranging from neurorehabilitation to assistive technologies. However, their performance depends critically on the accurate extraction of relevant neural features and the reliable recognition of underlying patterns. Deep learning has transformed this process. By automatically learning complex, task-relevant representations from raw or minimally processed EEG data, deep neural networks have surpassed many traditional handcrafted feature approaches in both accuracy and adaptability. Yet, the substantial computational and memory demands of many deep learning architectures limit their deployment in portable or real-time BCI systems. This challenge has motivated a growing interest in lightweight models-architectures optimized to reduce complexity while preserving or even enhancing performance. This paper provides a systematic review of such lightweight deep learning models for EEG signal classification. To organize this landscape, existing approaches are categorized into three main strategies: (1) information integration strategies based on multi-scale feature fusion, (2) hidden layer optimization strategies, and (3) hybrid improvement strategies based on structural optimization. The review synthesizes recent advances, identifies emerging trends, and outlines potential directions for future research. These insights aim to inform the design of efficient and robust EEG classification architectures capable of meeting the practical demands of real-world BCI applications.

Objective.Implanted electrodes for nerve stimulation and myoelectric recording facilitate bidirectional sensory feedback and control for neuromuscular conditions such as limb loss. While increasing implanted electrode channel count offers potential benefits, it also presents engineering and implementation challenges. This case study examines how increasing implanted electrode channel count affects sensory perception and myoelectric controller performance, thereby supporting the value of these advancements.Approach.One participant with upper extremity transradial limb loss received a percutaneous implanted system with two 8-channel extraneural cuff electrodes on the median and ulnar nerves, totaling 16 stimulating channels. The individual later received a wirelessly connected implanted system featuring four 16-channel extraneural cuff electrodes on the median, ulnar, and radial nerves, totaling 64 stimulation channels, and four tetra-intramuscular (TIM) recording electrodes in residual muscles, totaling 32 sensing channels configured into 16 bipolar pairs. To compare sensory perception between the 16- and 64-channel stimulation systems, we assessed cumulative percept location coverage and the number of unique percept locations, estimated through hierarchical clustering. We compared performance across three myoelectric control algorithms that mapped 8, 10, or 14 intramuscular signal inputs through an artificial neural network to control a virtual hand in 4 degrees-of-freedom (DOFs), with simultaneous, independent, and proportional control.Main results.Increasing stimulation channel count expanded cumulative percept location coverage and increased the number of unique percept locations on the hand. Adding intramuscular recording channel inputs improved 4-DOF myoelectric control of a virtual hand, increasing target posture match percentage and path efficiency.Significance.This case study demonstrates that increasing the number of implanted electrodes can advance sensory restoration and myoelectric control for bidirectional upper limb prostheses. Continued development of more complex systems with higher channel counts may further improve outcomes for individuals with limb loss and enhance the function of sensorimotor restoration systems.Trial registration:ClinicalTrials.gov ID: NCT04430218, 2020-06-30.

Objective.Brain networks communicate through long range phase coupling of low frequency oscillations (LFOs, less than 35 Hz) between brain regions. At the same time, phase-amplitude cross-frequency coupling (CFC), in which the phase of the same LFO have been shown to modulate the power of high frequency activity has also been reported across brain regions as a critical regulator of neural activity and excitability. While cross-regional CFC has been reported as a potential mechanism of long-distance modulation of neural excitability, the mechanism underlying this phenomenon has yet to be understood and methods to dissociate the effect of local vs remote LFO have not been developed. Cross-regional CFC can be a result of either LFOs in one region directly modulating high frequency oscillations in another region or due to a chain effect, in which apparent cross-regional CFC results from coupling of LFO across sites.Approach.A novel method of partial modulation index (PMI) is proposed as a derivation of modulation index (MI) and based on Pearl's do-calculus to remove the mathematical bias of simultaneous phase coupling and CFC measurements. Here, we first test the PMI on a simulated dataset, showing it can differentiate between biased and unbiased CFC. We then evaluate the method on intracranially collected local field potentials recorded simultaneously from thalamus and cortex in a patient undergoing deep brain stimulator implantation for essential tremor, demonstrating that the observed thalamocortical CFC was partially biased.Main results.For both simulated and human datasets, the PMI was compared to the conventional MI. In simulated data, the PMI was able to disentangle cross-regional phase coupling and focal CFC which is not possible using conventional MI. While there is no ground truth for comparison in human data, the results from the simulated data demonstrate the value of the proposed method in removing mathematical bias.Significance.This novel method facilitates a mathematically rigorous characterization of residual CFC, enabling investigations of differential contributions and roles of brain-wide LFO to CFC, which can lead to a more complete understanding of the pathophysiology of neurological processes and disorders.

Objective. A novel phase-amplitude coupling (PAC) estimator is proposed to address the limitations of existing PAC estimators in terms of insufficient application scenarios.Approach. The polar mutual information (PoMI) method is compared with the currently dominant PAC estimators, mean vector length, Kullback-Libler distance, general linear model, and phase-locking value, focusing on analyzing its characteristics in terms of coupling strength sensitivity, data length dependency, noise resistance, and coupling frequency band sensitivity. We recruited 54 healthy controls and 41 mild cognitive impairment (MCI) patients and assessed their cognitive level and whole-brain PAC connectivity by neurophysiological tests and resting electroencephalography, respectively.Main results. The PoMI algorithm is sensitive to changes in coupling strength, exhibits low dependence on data length, is insensitive to noise variations, and produces stable computational outcomes. Therefore, the PoMI algorithm can quantify the PAC phenomenon in neural oscillations. Furthermore, reduced PAC connectivity in the frontal lobe of patients with MCI, while PAC activity is enhanced in the parietal and occipital lobes. The results indicate that alterations in prefrontal PAC connectivity in MCI patients may represent one manifestation of neuronal group degeneration in the prefrontal cortex of these individuals.Significance. The PoMI algorithm can effectively evaluate the PAC phenomenon in neural oscillations and can be used as a PAC estimator. (Approved No. of ethic committee: 2024-P2-210-02).

Objective.Group interactions capture cooperative dynamics among neural populations quantitatively, while also enabling precise detection of ensemble-level synchrony patterns and transcending the limitations of node-level relationships. To evaluate higher-order group interactions, we propose the PLASSO-homophily framework using multichannel stereo-electroencephalography (SEEG) recorded from patients with epilepsy.Approach.Specifically, we use phase locking value to improve least absolute shrinkage and selection operator method for constructing hypergraphs. Afterwards, we calculate affinity ratios between brain zones. Finally, we investigate higher-order interactions among different groups from a homophily perspective. The extremal result of strict homophily serves as a crucial theoretical framework for understanding homophily concepts, reflecting the constraints that different groups follow in higher-order interactions.Main results.It is observed that group interactions between seizure onset zones (SOZ), propagation zones (PZ) and non-involved zones (NIZ) present significant distinction across different seizure phases. In particular, the homophily of SOZ reaches a peak point during the seizure and sharply decreases in the post-seizure, with the most statistically significant differences onθandγbands. Furthermore, during the seizure, SOZ-PZ exhibits enhanced coupling while SOZ-NIZ exhibits impaired functional integration. Finally, among three groups, only SOZ exhibits strict monotonic and majority homophily.Significance.By analyzing changes in in-class and out-class connectivity, we quantitatively assess the activity levels and combinatorial constraints of the SOZ, PZ, and NIZ, thereby providing a novel perspective for exploring seizure mechanisms and developing epilepsy treatments.

Objective.Transcutaneous spinal cord stimulation, a non-invasive spinal cord neuromodulation method holds tremendous promise and hope to restore functions in individuals with paralysis resulting from spinal cord injury (SCI), cerebral palsy, stroke and other neurological conditions. Yet, there are relatively few options for such stimulation devices compared to conventional stimulators commonly used for neuromuscular electrical stimulation, transcutaneous electrical nerve stimulation, and functional electrical stimulation, particularly for people with neurological conditions in the developing countries.Approach.In this report, we present OpenXstim, an open-source, two-channel programmable electrical stimulator developed to advance research in non-invasive muscle, nerve, or spinal cord stimulation treatments.Main results. OpenXstim can deliver current pulses up to 110 mA with a compliance voltage of 96 V per channel. In benchtop testing, we found that the stimulator successfully generates high frequency (9 kHz) burst stimulation, a mode commonly used for spinal cord neuromodulation. The stimulator was further tested in two individuals with SCI and showed preliminary indications of functional improvement. However, large controlled trials are needed to establish efficacy. Although special care was taken in the design of the stimulator to ensure user safety, users are strongly warned to handle the device with utmost caution, as it can generate high voltage and current that may cause adverse health effects if not used properly.Significance.This programmable, open-source stimulator offers tangible hope for improving the accessibility of non-invasive neuromodulation treatments for people with paralysis worldwide. The design and complete source-code of the stimulator are freely available online in a public repository:https://github.com/OpenMedTech-Lab/OpenXstim.

Objective.As the prevalence of dementia continues to rise, the need for accurate and early diagnostic tools becomes increasingly critical. Despite diverse underlying causes, dementia types share common cognitive symptoms, making accurate diagnosis essential for effective treatment.Approach: This study investigates electroencephalographic (EEG)-based spectral brain connectivity in individuals with Alzheimer's disease (AD,N=36), frontotemporal dementia (FTD,N=23), and healthy controls (HCs,N=29), with the dual aim of identifying condition-specific connectivity patterns and evaluating three coherence-based connectivity measures: coherence, imaginary coherence, and partial coherence. Resting-state, eyes-closed EEG data (19 channels) were analyzed, and connectivity was estimated across frequencies to assess both global and local network alterations.Main results.The results indicate that dementias (both AD and FTD) are characterized by decreased connectivity in higher frequency bands and increased connectivity in lower frequencies, reflecting respectively impaired neural communication and neurodegeneration. Moreover, the severity of cognitive impairment correlates with the spatial extent and magnitude of connectivity disruptions. Notably, partial coherence-unlike coherence and imaginary coherence-effectively distinguishes between the AD and FTD groups, suggesting that direct connectivity measures may provide more discriminative information for differential diagnosis.Significance.These findings highlight the potential of EEG-based spectral connectivity analysis, particularly partial coherence, as a non-invasive tool to aid in the diagnosis and differential diagnosis of dementia subtypes, supporting early clinical decision-making.

Objective.Domain adaptation (DA) has achieved remarkable performance in cross-subject electroencephalogram (EEG) decoding by mitigating the inter-subject data distribution discrepancies. However, when exploring the feature alignment subspace and performing self-supervised pseudo-labeling in an iterative way, two difficulties are often encountered: one is that unreliable target labeling results inevitably mislead the domain-free feature learning process in the early stage and the other is that the contribution of source and target samples should be balanced in the later stage.Approach.To address both issues, this paper proposes prototype-based progressive confident target sample labeling (P²CSL) method to use subspace class prototypes to assist in labeling target samples under the unified framework of domain-invariant EEG feature learning and the self-supervised target sample labeling, and progressively incorporate confident target samples into DA model fitting. The underlying rationality is that early-stage pseudo-labels from unconverged models are prone to error propagation, requiring auxiliary mechanisms to ensure their reliability and stabilize training. With the gradual alignment of cross-subject features, the estimated pseudo-label information of target domain will be more reliable, meaning that more target samples should be involved in model training.Main results.Experiments on emotion recognition and inner speech decoding demonstrate the competitive performance of P²CSL in cross-subject EEG classification in comparison with SOTA methods.Significance.Our study indicates the effectiveness of jointly considering the reliability of target samples and their contribution to model training in the context of DA. In addition, some fine-grained results including the sample confidence allocation strategy, the DA effects, and the dynamic model optimization process are provided to further illustrate the model execution details.

Obective.Bipolar re-referencing (BPRR), in which one electrode's signal is subtracted from a neighboring electrode to produce a differential signal, can improve signal readability and refine localization for intracranial electroencephalography. There is wide variation in manufactured electrode array spacing, yet how BPRR affects specific frequencies at precise inter-electrode distances has not been systematically evaluated.Approach.Intracranial recordings with uniquely large numbers of electrodes were obtained for sixteen patients with drug-resistant epilepsy. We evaluated combinations of high-density subdural grid, depth, and strip electrodes (n= 3,664, 742, and 336) with manufactured linear inter-electrode distances of 4, 5, and 10 mm, respectively. BPRR was performed using all possible electrode pairs (n= 445 305 grid, 16 004 depth, 3278 strip) spanning distances from 2-60 mm. Multi-taper power spectra were generated separately for grid, depth, and strip contacts. Distances were consolidated across patients and anatomical areas for generalizability, and distance-related influences on task-related brain activity and quantitative interictal epileptiform discharge localization were evaluated.Main results.We identified 8 mm as a consistent reversal point for BPRR, below which low-frequency signals (<30 Hz) had consistently decreased power, and higher frequencies had increased power. Larger distances increased all broadband (2-200 Hz) signals. Task-related increases in superior temporal gyrus 50-200 Hz activity were consistently enhanced across 4-40 mm bipolar distances. There were non-significant difference trends between 4 and 8 mm re-referencing on epileptiform discharge detection.Significance.BPRR distance imposed specific transition points for distance and frequency (roughly 8 mm and ∼30 Hz, respectively) that produced differential effects on measurements of signal power. The consistency across brain regions and electrode types (depth, subdural) suggests these influences are physical brain bio-signal properties, potentially related to spatial wavelength of periodic oscillations in lower frequencies in contrast to more aperiodic activity in higher frequencies. A distance-frequency relation map is provided to help optimize neural signal biomarker quality for intracranial applications by guiding strategic re-referencing distance selection.