Journal of neural engineering最新文献

Objective.In brain-computer interface (BCI) applications, signals are conventionally acquired from the cerebrum, and only a subset of the complex interactions that occur in several areas of the brain are collected. One area that has not been investigated for BCI application is the cerebellum, despite its involvement in movement and executive function. The present study aimed to determine the features of movement-related cerebellar electrocerebellography (ECeG) that are most useful for decoding, and how performance compares with conventional electroencephalography (EEG) recordings from the cerebrum.Approach.ECeG and EEG data were collected from six healthy adults to identify useful movement-related features from both cerebrum and cerebellum. Electromyography was used to capture the movements from the muscles. Decoding was conducted in binary movement vs. rest and movement vs. movement systems using support vector machines. Decoding performance was compared between cerebral, cerebellar, a combination of both, and temporal groups. Re-referencing techniques were applied to compensate for possible common reference artefacts or volume conduction effects.Main results. Movement-related features were decoded from over the cerebellum and the cerebrum. Classification accuracies were similar in both the cerebrum and cerebellum, when classifying movement vs. rest (cerebrum: 0.78 ± 0.02, cerebellum: 0.70 ± 0.01) and movement vs. movement states (cerebrum: 0.76 ± 0.02, cerebellum: 0.71 ± 0.02). The delta band (1-3 Hz) was the most useful feature for decoding.Significance.This study demonstrated, for the first time, that ECeG is a feasible source of movement related signals for implementing a BCI. The present study also demonstrated that the ECeG closely resembled the EEG signals and represents an alternate approach for BCI where the signal from the cerebrum is unreliable either due to disease or injury.

Objective. The goal of this study was to examine the effects of spinal cord stimulation (SCS) on muscle activity during walking after lower-limb amputation. Amputation results in a loss of sensory feedback and alterations in gait biomechanics, including co-contractions of antagonist muscles about the knee and ankle, and reduced pelvic obliquity range-of-motion and pelvic drop. SCS can restore sensation in the missing limb, but its effects on muscle activation and gait biomechanics have not been studied in people with lower-limb amputation.Approach. This case study included a participant with transtibial amputation who was implanted percutaneously with SCS electrodes over the lumbosacral enlargement for 84 d. SCS was used during in-lab experiments to provide somatosensory feedback from the missing limb, relaying a sense of plantar pressure when the prosthesis was in the stance phase of the gait cycle. We used electromyography (EMG) to record muscle activity from the residual and intact limbs, and 3D motion capture to measure pelvic obliquity and knee and ankle joint angles. EMG signals were recorded during walking with and without SCS at early (Day 30) and late (Day 63) time points across the implant duration.Main results. During walking, co-contraction of knee antagonist muscles was reduced following multiple sessions of SCS-mediated sensory restoration. Additionally, the activation of the hip abductor (tensor fasciae latae) muscle increased activity during gait with SCS-mediated sensory restoration, which corresponded to an increase in pelvic obliquity range-of-motion and pelvic drop, towards normal.Significance. Restoring sensation in the missing limb using SCS altered muscle activity during walking led to improved coordination and pelvic motion in an individual with lower-limb amputation.

Objective.Magnetomyography (MMG) using optically pumped magnetometers (OPM) offers a contactless, non-invasive approach to assess muscle activity. However, fluctuations in the sensor-to-source distance during MMG recordings pose a significant challenge to accurate signal interpretation since amplitude decays with distance. No established method exists for MMG to continuously monitor sensor-to-source distance changes in real-time.Approach.This study presents a new non-magnetic, cost-effective solution using a digital fiber optic sensor to continuously measure the distance between an OPM and the subject's skin. Following sensor calibration, distance measurements were recorded during an isometric muscle fatigue task in five healthy participants to assess whether MMG amplitude changes were due to physiological effects or variations in sensor-to-source distance. Alongside OPM-MMG and distance tracking, electromyography (EMG), the neurophysiological gold standard, was simultaneously recorded.Main results.We found significant changes in MMG-RMS and MMG-MDF during muscle fatigue that were not merely explained by changes in sensor-to-source distance. Furthermore, we found substantial correlations between OPM-MMG and EMG that were strongest for small sensor-to-source distance (r= 0.91).Significance.Fiber optic sensors offer non-magnetic, precise, real-time monitoring of the distance between the OPM and the skin, making it ideal for MMG applications to account for distance-related variability during measurements. Our results suggest that changes in MMG-RMS and MMG-MDF during muscle fatigue reflect genuine physiological effects rather than distance confounds.

Objective. Several data-driven approaches based on information theory have been proposed for analyzing high-order interactions (HOIs) involving three or more components of a network system. The existing methods do not account for temporal correlations in the data, or are defined only in the time domain and rely on the assumption of stationarity in the underlying dynamics, making them inherently unable to detect frequency-specific behaviors and track transient functional links in physiological networks.Approach. This study introduces a new framework which enables the time-varying and time-frequency analysis of HOIs in networks of random processes through the spectral representation of vector autoregressive models. The time- and frequency-resolved analysis of synergistic and redundant interactions among groups of processes is ensured by a robust identification procedure based on a recursive least squares estimator with a forgetting factor.Main results. Validation on simulated networks illustrates how the time-frequency analysis is able to highlight transient synergistic behaviors emerging in specific frequency bands which cannot be detected by time-domain stationary analyzes. The application on brain evoked potentials in rats elicits the presence of redundant information timed with whisker stimulation and mostly occurring in the contralateral hemisphere. The application to cardiovascular oscillations reveals a reduction in redundant information following head-up tilt, reflecting a functional disconnection within the physiological network of heart period, respiratory, and arterial pressure signals.Significance. The proposed framework enables a comprehensive time-varying and time-frequency analysis of the hierarchical organization of dynamic networks. As our approach goes beyond pairwise interactions, it is well suited for the study of transient high-order behaviors arising during state transitions in many network systems commonly studied in physiology, neuroscience and other fields.

Tourette syndrome (TS) is a chronic tic disorder characterized by motor and vocal tics. Neuropsychiatric symptoms are nearly universal in TS, particularly attention deficit hyperactivity disorder and obsessive-compulsive disorder. TS can have substantial effects on quality of life, social and intellectual development, opportunities, relationships, and more. Treatment options are limited; the most common being behavioral therapy and pharmacological interventions, such as antipsychotics and anti-adrenergic agents, often yielding unsatisfactory benefits. Neuromodulation, the alteration of neural pathways and networks under external stimulation, has been established as a viable treatment strategy for specific aspects of TS. Several neuromodulation techniques have been utilized, with deep brain stimulation (DBS) exhibiting the strongest efficacy at around 50% reduction of tics on average across cohorts. However, the invasive nature of DBS remains a disincentive for its uptake, as well as the natural reduction in tic severity for many TS individuals as they enter adulthood. Less-invasive neuromodulation has also been explored, but efficacy remains limited. Given its effectiveness in TS, DBS provides the unique opportunity to record neural activity from deep brain structures, which has been used to investigate underlying pathophysiology and search for biomarkers of treatment response. These insights may guide strategies for less invasive neuromodulation. In this narrative review, we aim to discuss currently utilized neuromodulation therapies for the treatment of TS, as well as propose potential future strategies. Additionally, we discuss how to maximize progress in the field, including crucial multicenter data sharing, utilization of recording capabilities on DBS devices, correlation with the precise location of implanted electrodes, and harnessing pre-clinical studies for a more parameterized understanding of TS neuromodulation. These techniques will enable a clearer understanding of TS and the mechanisms behind successful treatment. This could lead to advanced therapies that improve the quality of life for individuals with TS.

Objective.This paper presents an in-depth analysis of the recent literature on dynamic functional connectivity (dFC) analysis. This represents a paradigm shift in the analysis of neural data to overcome the inherent limitations of static assumptions about functional brain connectivity. By exploiting the information provided by high temporal resolution neuroimaging techniques, such as magnetoencephalography (MEG) and electroencephalography (EEG), the possibility of tracking functional network organization and reconfiguration that support brain functions at different temporal scales has been extensively explored.Approach.This review examines the current state-of-the-art of the methodological approaches for dFC analysis in biomedical science, focusing on literature from 2018 to 2024 and on the analysis of EEG and MEG data. The review primarily concentrates on methods for estimating the time-resolved functional connectivity matrix, also providing an overview of approaches for summarising and inferring dynamic information.Main results.An insight into the available methodological approaches for tracking dFC at different temporal scales is offered. Besides the classical sliding window method, advances in instantaneous dFC algorithms are described and two novel approaches are introduced: microstate-based dFC (micro-dFC) and data-driven dFC methods. For each approach, specific features are detailed, and the dataset characteristics to ensure applicability are discussed. In addition, possible post-processing procedures for extracting the dynamic properties and information of interest are presented.Significance.The undoubted potential of dFC analysis for the study of brain dynamics is highlighted, providing a guide for its application, also taking into consideration the study protocol, the nature of the data and the temporal resolution of interest. Current limitations and open challenges are also critically addressed.

Objective.In this paper, we propose a novel neural network architecture, the convolutional spider neural network (CS-Net), combined with a transfer learning (TL) strategy, to classify hybrid gestures that integrate wrist postures and hand movements.Approach.The CS-Net framework incorporates diverse surface electromyography (sEMG) features, including raw signals and FFT representations, through a multi-stream information fusion mechanism to enhance classification performance. The proposed TL strategy involves pre-training the model on specific wrist postures and fine-tuning it on the full set of hybrid gestures, leveraging the intrinsic relationships between composite gestures and their constituent movements to improve accuracy. The framework is evaluated through extensive offline experiments using a dataset of 12 hybrid gestures (combining three wrist postures and four hand movements) collected from six subjects, comparing its performance against three deep learning algorithms in sEMG recognition filed.Main results.The average experimental result for the proposed CS-Net with TL reached 90.6%. Additionally, its generalization ability is validated with the Ninapro public databases, which are DB1, DB4, and DB5. The 30 action classification accuracy of CS-Net on the Ninapro datasets was 68.7%, 61.5% and 66.3%, respectively. To demonstrate practical applicability, real-time online experiments involving object grasping tasks is conducted, achieving a success rate of 90%.Significance.The results show that CS-Net significantly improves sEMG classification accuracy, while the TL strategy further enhances performance. Moreover, the algorithm achieved a high success rate in online experiments, confirming its robustness and practical utility for real-world applications. Our hybrid gesture dataset and source codes are available on Github.

Objective.High-resolution functional magnetic resonance imaging (fMRI) is essential for mapping human brain activity; however, it remains costly and logistically challenging. If comparable volumes could be generated directly from widely available scalp electroencephalography (EEG), advanced neuroimaging would become significantly more accessible. Existing EEG-to-fMRI generators rely on plain convolutional neural networks that fail to capture cross-channel time-frequency cues or on heavy transformer/generative adversarial network decoders that strain memory and stability.Approach.To address these limitations, we propose Spec2VolCAMU-Net, a lightweight architecture featuring a Multi-directional Time-Frequency Convolutional Attention Encoder for rich feature extraction and a Vision-Mamba U-Net decoder that uses linear-time state-space blocks for efficient long-range spatial modeling. We frame the goal of this work as establishing a new state of the art in the spatial fidelity of single-volume reconstruction, a foundational prerequisite for the ultimate aim of generating temporally coherent fMRI time series.Main results.Trained end-to-end with a hybrid SSI-MSE loss, Spec2VolCAMU-Net achieves state-of-the-art fidelity on three public benchmarks, recording structural similarity index (SSIM) of 0.693 on NODDI, 0.725 on Oddball and 0.788 on CN-EPFL, representing improvements of 14.5%, 14.9%, and 16.9% respectively over previous best SSIM scores. Furthermore, it achieves competitive peak signal-to-noise ratio (PSNR) scores, particularly excelling on the CN-EPFL dataset with a 4.6% improvement over the previous best PSNR, thus striking a better balance in reconstruction quality.Significance.The proposed model is lightweight and efficient, making it suitable for real-time applications in clinical and research settings.The code is available athttps://github.com/hdy6438/Spec2VolCAMU-Net.

Recent computational, pre-clinical, and clinical studies have demonstrated the potential for using neuromodulation through simultaneous targeting of multiple deep brain regions. This approach has already been used for therapeutic and systems neuroscience applications. However, the broad clinical adoption of invasive distributed deep brain interfaces remains in its early stages. This review explores the barriers to implementation by addressing three key questions: do the benefits of implanting multiple electrodes justify the associated risks for specific applications? What is the risk-benefit ratio, and what technological advancements will be necessary to encourage clinical adoption? We also examine next-generation technologies that could enable multi-target brain interfaces, including system-on-chip micro-stimulators as well as nanoparticles. We highlight the role of novel machine learning algorithms in the optimization of stimulation parameters and for the guidance of device placement. Emerging hardware accelerators equipped with on-chip AI have demonstrated functionality that can be used to decode and to classify distributed neuronal data. This advance in hardware accelerators has also contributed to the potential for enhanced closed-loop stimulation control of devices. Despite these advances, significant technological and translational barriers persist, limiting the widespread clinical application of multi-target brain interfaces. This review provides a critical analysis of recent prototypes and novel hardware for use in multi-target systems. We will discuss both clinical and research applications. We will focus on the utilization of multi-site technologies to meet the needs of neurological diseases. We conclude that there exists a critical need for further innovation and integration of multi-site technologies into clinical practice.

Objective.Accurate detection and classification of high-frequency oscillations (HFOs) in electroencephalography (EEG) recordings have become increasingly important for identifying epileptogenic zones in patients with drug-resistant epilepsy. However, few open-source platforms offer both state-of-the-art computational methods and user-friendly interfaces to support practical clinical use.Approach.We present PyHFO 2.0, an enhanced open-source, Python-based platform that extends previous work by incorporating a more comprehensive set of detection methods and deep learning (DL) tools for HFO analysis. The platform now supports three commonly used detectors: short-term energy, Montreal Neurological Institute, and a newly integrated Hilbert transform-based detector. For HFO classification, PyHFO 2.0 includes DL models for artifact rejection, spike HFO detection, and identification of epileptogenic HFOs. These models are integrated with the Hugging Face ecosystem for automatic loading and can be replaced with custom-trained alternatives. An interactive annotation module enables clinicians and researchers to inspect, verify, and reclassify events.Main results.All detection and classification modules were evaluated using clinical EEG datasets, supporting the applicability of the platform in both research and translational settings. Validation across multiple datasets demonstrated close alignment with expert-labeled annotations and standard tools such as RIPPLELAB.Significance.PyHFO 2.0 aims to simplify the use of computational neuroscience tools in both research and clinical environments by combining methodological rigor with a user-friendly graphical interface. Its scalable architecture and model integration capabilities support a range of applications in biomarker discovery, epilepsy diagnostics, and clinical decision support, bridging advanced computation and practical usability.