IEE Transactions on Neural Systems and Rehabilitation Engineering This 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.

DiffMDD: A Diffusion-Based Deep Learning Framework for MDD Diagnosis Using EEG
Major Depression Disorder (MDD) is a common yet destructive mental disorder that affects millions of people worldwide. Making early and accurate diagnosis of it is very meaningful. Recently, EEG, a non-invasive technique of recording spontaneous electrical activity of brains, has been widely used for MDD diagnosis. However, there are still some challenges in data quality and data size of EEG: (1) A large amount of noise is inevitable during EEG collection, making it difficult to extract discriminative features from raw EEG; (2) It is difficult to recruit a large number of subjects to collect sufficient and diverse data for...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 31, 2024 Category: Neuroscience Source Type: research

Temporal Alpha Dissimilarity of ADHD Brain Network in Comparison With CPT and CATA
Attention deficit hyperactivity disorder (ADHD) is a chronic neurological and psychiatric disorder that affects children during their development. To find neural patterns for ADHD and provide subjective features as decision references to assist specialists and physicians. Many studies have been devoted to investigating the neural dynamics of the brain through resting-state or continuous performance tests (CPT) with EEG or functional magnetic resonance imaging (fMRI). The present study used coherence, which is one of the functional connectivity (FC) methods, to analyze the neural patterns of children and adolescents (8-16 y...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 30, 2024 Category: Neuroscience Source Type: research

Rejecting Unknown Gestures Based on Surface-Electromyography Using Variational Autoencoder
The conventional surface electromyography (sEMG)-based gesture recognition systems exhibit impressive performance in controlled laboratory settings. As most systems are trained in a closed-set setting, the systems’s performance may see significant deterioration when novel gestures are presented as imposter. In addition, the state-of-the-art generative and discriminative methods have achieved considerable performance on high-density sEMG signals. This can be seen as an unrealistic setting as the real-world muscle computer interface are mainly comprised of sparse multichannel sEMG signals. In this work, we propose a novel ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 30, 2024 Category: Neuroscience Source Type: research

Subject-Independent Deep Architecture for EEG-Based Motor Imagery Classification
Motor imagery (MI) classification based on electroencephalogram (EEG) is a widely-used technique in non-invasive brain-computer interface (BCI) systems. Since EEG recordings suffer from heterogeneity across subjects and labeled data insufficiency, designing a classifier that performs the MI independently from the subject with limited labeled samples would be desirable. To overcome these limitations, we propose a novel subject-independent semi-supervised deep architecture (SSDA). The proposed SSDA consists of two parts: an unsupervised and a supervised element. The training set contains both labeled and unlabeled data sampl...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 30, 2024 Category: Neuroscience Source Type: research

A Cross-Scale Transformer and Triple-View Attention Based Domain-Rectified Transfer Learning for EEG Classification in RSVP Tasks
Rapid serial visual presentation (RSVP)-based brain-computer interface (BCI) is a promising target detection technique by using electroencephalogram (EEG) signals. However, existing deep learning approaches seldom considered dependencies of multi-scale temporal features and discriminative multi-view spectral features simultaneously, which limits the representation learning ability of the model and undermine the EEG classification performance. In addition, recent transfer learning-based methods generally failed to obtain transferable cross-subject invariant representations and commonly ignore the individual-specific informa...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 29, 2024 Category: Neuroscience Source Type: research

A Novel Passive Shoulder Exoskeleton Using Link Chains and Magnetic Spring Joints
In this study, we propose a new type of passive shoulder exoskeleton that uses magnetic spring joint and link chain. The redundant degrees of freedom in the link chains enables to follow the shoulder joint movement in the horizontal direction, and the magnetic spring joint generates torque without additional parts in a compact form. Conventional exoskeletons experience a loss in the assisting torque when the center of shoulder rotation changed during arm elevation. Our exoskeleton minimizes the torque loss by customizing the installation height and initial angle of the magnetic spring joint. The performances of the propose...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 29, 2024 Category: Neuroscience Source Type: research

Cross-Subject Motor Imagery Decoding by Transfer Learning of Tactile ERD
For Brain-Computer Interface (BCI) based on motor imagery (MI), the MI task is abstract and spontaneous, presenting challenges in measurement and control and resulting in a lower signal-to-noise ratio. The quality of the collected MI data significantly impacts the cross-subject calibration results. To address this challenge, we introduce a novel cross-subject calibration method based on passive tactile afferent stimulation, in which data induced by tactile stimulation is utilized to calibrate transfer learning models for cross-subject decoding. During the experiments, tactile stimulation was applied to either the left or r...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 25, 2024 Category: Neuroscience Source Type: research

Automatic Assessment of Upper Extremity Function and Mobile Application for Self-Administered Stroke Rehabilitation
Rehabilitation training is essential for a successful recovery of upper extremity function after stroke. Training programs are typically conducted in hospitals or rehabilitation centers, supervised by specialized medical professionals. However, frequent visits to hospitals can be burdensome for stroke patients with limited mobility. We consider a self-administered rehabilitation system based on a mobile application in which patients can periodically upload videos of themselves performing reach-to-grasp tasks to receive recommendations for self-managed exercises or progress reports. Sensing equipment aside from cameras is t...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 25, 2024 Category: Neuroscience Source Type: research

Multi-Task Collaborative Network: Bridge the Supervised and Self-Supervised Learning for EEG Classification in RSVP Tasks
Electroencephalography (EEG) datasets are characterized by low signal-to-noise signals and unquantifiable noisy labels, which hinder the classification performance in rapid serial visual presentation (RSVP) tasks. Previous approaches primarily relied on supervised learning (SL), which may result in overfitting and reduced generalization performance. In this paper, we propose a novel multi-task collaborative network (MTCN) that integrates both SL and self-supervised learning (SSL) to extract more generalized EEG representations. The original SL task, i.e., the RSVP EEG classification task, is used to capture initial represe...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 24, 2024 Category: Neuroscience Source Type: research

Dual-3DM3AD: Mixed Transformer Based Semantic Segmentation and Triplet Pre-Processing for Early Multi-Class Alzheimer’s Diagnosis
Alzheimer’s Disease (AD) is a widespread, chronic, irreversible, and degenerative condition, and its early detection during the prodromal stage is of utmost importance. Typically, AD studies rely on single data modalities, such as MRI or PET, for making predictions. Nevertheless, combining metabolic and structural data can offer a comprehensive perspective on AD staging analysis. To address this goal, this paper introduces an innovative multi-modal fusion-based approach named as Dual-3DM3-AD. This model is proposed for an accurate and early Alzheimer’s diagnosis by considering both MRI and PET image scans. Initially, w...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 23, 2024 Category: Neuroscience Source Type: research

Analysis of the Relation Between Balance Control Subsystems: A Structural Equation Modeling Approach
This study provides the first numerical evidence that the BCSes are not independent of each other and exist in direct or indirect interplay. This approach has important implications for the diagnosis and management of balance-related disorders in clinical settings and improving our understanding of the underlying mechanisms of balance control. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 23, 2024 Category: Neuroscience Source Type: research

An Upper Limb Exoskeleton Motion Generation Algorithm Based on Separating Shoulder and Arm Motion
Many rehabilitation exoskeletons have been used in the field of stroke rehabilitation. Generating human-like motion is necessary for exoskeletons to help patients perform activities of daily living (ADL) while maintaining interaction quality and ergonomics. However, most of the current motion generation algorithms utilize inverse kinematics (IK) to solve the final configuration before generation, and do not consider the movement of shoulder girdle. Separately considering the shoulder girdle motion and arm motion, this paper proposes an algorithm integrated IK to generate human-like motion. The arm moves towards the target ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 22, 2024 Category: Neuroscience Source Type: research

Robotic Leg Prosthesis: A Survey From Dynamic Model to Adaptive Control for Gait Coordination
Gait coordination (GC), meaning that one leg moves in the same pattern but with a specific phase lag to the other, is a spontaneous behavior in the walking of a healthy person. It is also crucial for unilateral amputees with the robotic leg prosthesis to perform ambulation cooperatively in the real world. However, achieving the GC for amputees poses significant challenges to the prostheses’ dynamic modeling and control design. Still, there has not been a clear survey on the initiation and evolution of the detailed solutions, hindering the precise decision of future explorations. To this end, this paper comprehensively re...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 22, 2024 Category: Neuroscience Source Type: research

Multi-Scale FC-Based Multi-Order GCN: A Novel Model for Predicting Individual Behavior From fMRI
Predicting individual behavior from brain imaging data using machine learning is a rapidly growing field in neuroscience. Functional connectivity (FC), which captures interactions between different brain regions, contains valuable information about the organization of the brain and is considered a crucial feature for modeling human behavior. Graph convolutional networks (GCN) have proven to be a powerful tool for extracting graph structure features and have shown promising results in various FC-based classification tasks, such as disease classification and prognosis prediction. Despite this success, few behavior prediction...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 22, 2024 Category: Neuroscience Source Type: research

Motor Imagery Classification for Asynchronous EEG-Based Brain–Computer Interfaces
Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various body parts. Unlike previous systems that used fixed-length EEG trials for MI decoding, asynchronous BCIs aim to detect the user’s MI without explicit triggers. They are challenging to implement, because the algorithm needs to first distinguish between resting-states and MI trials, and then classify the MI trials into the correct task, all without any triggers. This paper proposes a sliding window prescreening and classification (SWPC) approach for MI-based asynchronous BCIs, wh...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 22, 2024 Category: Neuroscience Source Type: research