IEE Transactions on Neural Systems and Rehabilitation Engineering IEE Transactions on Neural Systems and Rehabilitation Engineering RSS feedThis is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Exploring Adaptive Graph Topologies and Temporal Graph Networks for EEG-Based Depression Detection
In recent years, Graph Neural Networks (GNNs) based on deep learning techniques have achieved promising results in EEG-based depression detection tasks but still have some limitations. Firstly, most existing GNN-based methods use pre-computed graph adjacency matrices, which ignore the differences in brain networks between individuals. Additionally, methods based on graph-structured data do not consider the temporal dependency information of brain networks. To address these issues, we propose a deep learning algorithm that explores adaptive graph topologies and temporal graph networks for EEG-based depression detection. Spe...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 29, 2023 Category: Neuroscience Source Type: research

Graph Signal Smoothness Based Feature Learning of Brain Functional Networks in Schizophrenia
In this paper we study the brain functional network of schizophrenic patients based on resting-state fMRI data. Different from the region of interest (ROI)-level brain networks that describe the connectivity between brain regions, this paper constructs a subject-level brain functional network that describes the similarity between subjects from a graph signal processing (GSP) perspective. Based on the subject graph, we introduce the concept of graph signal smoothness to analyze the abnormal brain regions (feature brain regions) in which schizophrenic patients produce abnormal functional connections and to quantitatively ran...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 28, 2023 Category: Neuroscience Source Type: research

Characterization of Motor Unit Firing and Twitch Properties for Decoding Musculoskeletal Force in the Human Ankle Joint In Vivo
Understanding how motor units (MUs) contribute to skeletal mechanical force is crucial for unraveling the underlying mechanism of human movement. Alterations in MU firing, contractile and force-generating properties emerge in response to physical training, aging or injury. However, how changes in MU firing and twitch properties dictate skeletal muscle force generation in healthy and impaired individuals remains an open question. In this work, we present a MU-specific approach to identify firing and twitch properties of MU samples and employ them to decode musculoskeletal function in vivo. First, MU firing events were decom...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 27, 2023 Category: Neuroscience Source Type: research

Movement Artifact Suppression in Wearable Low-Density and Dry EEG Recordings Using Active Electrodes and Artifact Subspace Reconstruction
Wearable low-density dry electroencephalogram (EEG) headsets facilitate multidisciplinary applications of brain-activity decoding and brain-triggered interaction for healthy people in real-world scenarios. However, movement artifacts pose a great challenge to their validity in users with naturalistic behaviors (i.e., without highly controlled settings in a laboratory). High-precision, high-density EEG instruments commonly embed an active electrode infrastructure and/or incorporate an auxiliary artifact subspace reconstruction (ASR) pipeline to handle movement artifact interferences. Existing endeavors motivate this study t...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 26, 2023 Category: Neuroscience Source Type: research

Haptic Human-Human Interaction During an Ankle Tracking Task: Effects of Virtual Connection Stiffness
In this study, our goal was to investigate whether these findings generalize to the lower limb during an ankle tracking task. Pairs of healthy participants (i.e., dyads) independently tracked target trajectories with and without connections rendered between two ankle robots. We tested the effects of connection stiffness as well as visual noise to manipulate the correlation of tracking errors between partners. In our analysis, we compared changes in task performance across conditions while tracking with and without the connection. We found that tracking improvements while connected increased with connection stiffness, favor...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 25, 2023 Category: Neuroscience Source Type: research

Spatial Variation Generation Algorithm for Motor Imagery Data Augmentation: Increasing the Density of Sample Vicinity
In this study, a Spatial Variation Generation (SVG) algorithm for MI data augmentation is proposed to alleviate the overfitting issue. In essence, SVG generates MI data using variations of electrode placement and brain spatial pattern, ultimately elevating the density of the raw sample vicinity. The proposed SVG prevents models from memorizing the training data by replacing the raw samples with the proper vicinal distribution. Moreover, SVG generates a uniform distribution and stabilizes the training process of models. In comparison studies involving five deep learning-based models across eight datasets, the proposed SVG a...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 20, 2023 Category: Neuroscience Source Type: research

Multimodal Autism Spectrum Disorder Diagnosis Method Based on DeepGCN
This study constructs a whole-brain functional connectivity network based on functional MRI data, uses non-imaging data with demographic information to complement the classification task for diagnosing subjects, and proposes a multimodal and across-site WL-DeepGCN-based method for classification to diagnose autism spectrum disorder (ASD). This method is used to resolve the existing problem that deep learning ASD identification cannot efficiently utilize multimodal data. In the WL-DeepGCN, a weight-learning network is used to represent the similarity of non-imaging data in the latent space, introducing a new approach for co...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 20, 2023 Category: Neuroscience Source Type: research

Cortical Contributions to Imagined Power Grip Task: An EEG-Triggered TMS Study
Previous studies have demonstrated that motor imagery leads to desynchronization in the alpha rhythm within the contralateral primary motor cortex. However, the underlying electrophysiological mechanisms responsible for this desynchronization during motor imagery remain unclear. To examine this question, we conducted an investigation using EEG in combination with noninvasive transcranial magnetic stimulation (TMS) during index finger abduction (ABD) and power grip imaginations. The TMS was administered employing diverse coil orientations to selectively stimulate corticospinal axons, aiming to target both early and late syn...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 20, 2023 Category: Neuroscience Source Type: research

Effects on Retinal Stimulation of the Geometry and the Insertion Location of Penetrating Electrodes
In this study, we used the finite element method to simulate electric fields created by 3D microelectrodes of three different designs in a retina model with a stratified conductivity profile. The three electrode designs included two conventional shapes - a conical electrode (CE) and a pillar electrode (PE); we also proposed a novel structure of pillar electrode with an insulating wall (PEIW). A quantitative comparison of these designs shows the PEIW generates a stronger and more confined electric field with the same current injection, which is preferred for high-resolution retinal prostheses. Moreover, our results demonstr...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 20, 2023 Category: Neuroscience Source Type: research

A Contrastive Learning Network for Performance Metric and Assessment of Physical Rehabilitation Exercises
Human activity analysis in the legal monitoring environment plays an important role in the physical rehabilitation field, as it helps patients with physical injuries improve their postoperative conditions and reduce their medical costs. Recently, several deep learning-based action quality assessment (AQA) frameworks have been proposed to evaluate physical rehabilitation exercises. However, most of them treat this problem as a simple regression task, which requires both the action instance and its score label as input. This approach is limited by the fact that the annotations in this field usually consist of healthy or unhe...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 20, 2023 Category: Neuroscience Source Type: research

Functional Connectivity Analysis of Transcutaneous Vagus Nerve Stimulation (tVNS) Using Magnetoencephalography (MEG)
Altered brain functional connectivity has been observed in conditions such as schizophrenia, dementia and depression and may represent a target for treatment. Transcutaneous vagus nerve stimulation (tVNS) is a form of non-invasive brain stimulation that is increasingly used in the treatment of a variety of health conditions. We previously combined tVNS with magnetoencephalography (MEG) and observed that various stimulation frequencies affected different brain areas in healthy individuals. We further investigated whether tVNS had an effect on functional connectivity with a focus on brain regions associated with mood. We com...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 19, 2023 Category: Neuroscience Source Type: research

Epileptic Seizure Detection and Prediction in EEGs Using Power Spectra Density Parameterization
Power spectrum analysis is one of the effective tools for classifying epileptic signals based on electroencephalography (EEG) recordings. However, the conflation of periodic and aperiodic components within the EEG may presents an obstacle to epilepsy detection or prediction. In this paper, we explored the significance of the periodic and aperiodic components of the EEG power spectrum for the detection and prediction of epilepsy respectively. We use a power spectrum density parameterization method to separate the periodic and aperiodic components of the signals, and validate their roles in epilepsy detection and prediction ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 19, 2023 Category: Neuroscience Source Type: research

A Novel Passive Back-Support Exoskeleton With a Spring-Cable-Differential for Lifting Assistance
Lower back injuries are the most common work-related musculoskeletal disorders. As a wearable device, a back-support exoskeleton (BSE) can reduce the risk of lower back injuries and passive BSEs can achieve a low device weight. However, with current passive BSEs, there is a problem that the user must push against the device when lifting the leg to walk, which is perceived as particularly uncomfortable due to the resistance. To solve this problem, we propose a novel passive BSE that can automatically distinguish between lifting and walking. A unique spring-cable-differential acts as a torque generator to drive both hip join...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 19, 2023 Category: Neuroscience Source Type: research

Visual Occlusions Result in Phase Synchrony Within Multiple Brain Regions Involved in Sensory Processing and Balance Control
There is a need to develop appropriate balance training interventions to minimize the risk of falls. Recently, we found that intermittent visual occlusions can substantially improve the effectiveness and retention of balance beam walking practice (Symeonidou & Ferris, 2022). We sought to determine how the intermittent visual occlusions affect electrocortical activity during beam walking. We hypothesized that areas involved in sensorimotor processing and balance control would demonstrate spectral power changes and inter-trial coherence modulations after loss and restoration of vision. Ten healthy young adults practiced walk...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 19, 2023 Category: Neuroscience Source Type: research

Multi-Channel FES Gait Rehabilitation Assistance System Based on Adaptive sEMG Modulation
This study proposes a multi-channel FES gait rehabilitation assistance system based on adaptive myoelectric modulation. The proposed system collects sEMG of the vastus lateralis muscle on the non-affected side to predict the sEMG values of four targeted lower-limb muscles on the affected side using a bidirectional long short-term memory (BILSTM) model. Next, the proposed system modulates the real-time FES output frequency for four targeted muscles based on the predicted sEMG values to provide muscle force compensation. Fifteen healthy subjects were recruited to participate in an offline model-building experiment conducted ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 18, 2023 Category: Neuroscience Source Type: research