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.

Reducing Motor Variability Enhances Myoelectric Control Robustness Across Untrained Limb Positions
The limb position effect is a multi-faceted problem, associated with decreased upper-limb prosthesis control acuity following a change in arm position. Factors contributing to this problem can arise from distinct environmental or physiological sources. Despite their differences in origin, the effect of each factor manifests similarly as increased input data variability. This variability can cause incorrect decoding of user intent. Previous research has attempted to address this by better capturing input data variability with data abundance. In this paper, we take an alternative approach and investigate the effect of reduci...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 15, 2023 Category: Neuroscience Source Type: research

ADFCNN: Attention-Based Dual-Scale Fusion Convolutional Neural Network for Motor Imagery Brain–Computer Interface
Convolutional neural networks (CNNs) have been successfully applied to motor imagery (MI)-based brain–computer interface (BCI). Nevertheless, single-scale CNN fail to extract abundant information over a wide spectrum from EEG signals, while typical multi-scale CNNs cannot effectively fuse information from different scales with concatenation-based methods. To overcome these challenges, we propose a new scheme equipped with attention-based dual-scale fusion convolutional neural network (ADFCNN), which jointly extracts and fuses EEG spectral and spatial information at different scales. This scheme also provides novel insigh...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 13, 2023 Category: Neuroscience Source Type: research

Abnormal Low-Frequency Corticokinematic Coherence in Stroke: An Electroencephalography and Acceleration Study
Motor control is a complex process of coordination and information interaction among neural, motor, and sensory functions. Investigating the correlation between motor-physiological information helps to understand the human motor control mechanisms and is important for the assessment of motor function status. In this manuscript, we investigated the differences in the neuromotor coupling analysis between healthy controls and stroke patients in different movements. We applied the corticokinematic coherence (CKC) function between the electroencephalogram (EEG) and acceleration (ACC) data. First, we collected the EEG and ACC da...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 13, 2023 Category: Neuroscience Source Type: research

Mordo2: A Personalization Framework for Silent Command Recognition
Wearable human-computer interactions in daily life are increasingly encouraged by the prevalence of intelligent wearables. It poses a demanding requirement of micro-interaction and minimizing social awkwardness. Our previous work demonstrated the feasibility of recognizing silent commands through around-ear biosensors with the limitation of user adaptation. In this work, we ease the limitation by a personalization framework that integrates spectral factorization of signals, temporal confidence rejection and commonly used transfer learning algorithms. Specifically, we first empirically formulate the user adaptation issue by...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 12, 2023 Category: Neuroscience Source Type: research

Cross-User Electromyography Pattern Recognition Based on a Novel Spatial-Temporal Graph Convolutional Network
With the goal of promoting the development of myoelectric control technology, this paper focuses on exploring graph neural network (GNN) based robust electromyography (EMG) pattern recognition solutions. Given that high-density surface EMG (HD-sEMG) signal contains rich temporal and spatial information, the multi-view spatial-temporal graph convolutional network (MSTGCN)is adopted as the basic classifier, and a feature extraction convolutional neural network (CNN) module is designed and integrated into MSTGCN to generate a new model called CNN-MSTGCN. The EMG pattern recognition experiments are conducted on HD-sEMG data of...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 12, 2023 Category: Neuroscience Source Type: research

Cross-Domain Identification of Multisite Major Depressive Disorder Using End-to-End Brain Dynamic Attention Network
Establishing objective and quantitative imaging markers at individual level can assist in accurate diagnosis of Major Depressive Disorder (MDD). However, the clinical heterogeneity of MDD and the shift to multisite data decreased identification accuracy. To address these issues, the Brain Dynamic Attention Network (BDANet) is innovatively proposed, and analyzed bimodal scans from 2055 participants of the Rest-meta-MDD consortium. The end-to-end BDANet contains two crucial components. The Dynamic BrainGraph Generator dynamically focuses and represents topological relationships between Regions of Interest, overcoming limitat...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 12, 2023 Category: Neuroscience Source Type: research

Center of Mass Estimation for Impaired Gait Assessment Using Inertial Measurement Units
Injury or disease often compromise walking dynamics and negatively impact quality of life and independence. Assessing methods to restore or improve pathological gait can be expedited by examining a global parameter that reflects overall musculoskeletal control. Center of mass (CoM) kinematics follow well-defined trajectories during unimpaired gait, and change predictably with various gait pathologies. We propose a method to estimate CoM trajectories from inertial measurement units (IMUs) using a bidirectional Long Short-Term Memory neural network to evaluate rehabilitation interventions and outcomes. Five non-disabled volu...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 12, 2023 Category: Neuroscience Source Type: research

From Forearm to Wrist: Deep Learning for Surface Electromyography-Based Gesture Recognition
This study compared the gesture recognition performance of myoelectric signals from the wrist and forearm between a state-of-the-art method, TDLDA, and four deep learning models, including convolutional neural network (CNN), temporal convolutional network (TCN), gate recurrent unit (GRU) and Transformer. It was shown that with forearm myoelectric signals, the performance between deep learning models and TDLDA was comparable, but with wrist myoelectric signals, the deep learning models outperformed TDLDA significantly with a difference of at least 9%, while the performance of TDLDA was close between the two signal modalitie...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 8, 2023 Category: Neuroscience Source Type: research

Variation Minimization Based Electrocardiogram Artifacts Removal for Local Field Potentials From Neurostimulator
This study proposed a novel algorithm based on minimizing the variance combining template subtraction to improve the performance of ECG artifact removal for LFP. Four patients with implanted electrodes were recruited, and eight real LFP records were collected from their left and right hemispheres, respectively. The results showed that the proposed method improved the accuracy of artifact peak detection in LFP, and the subsequent signal quality after template subtraction compared to the traditional Pan-Tompkins (PT) method. The outcome of this study benefited the LFP-based brain research, promoting the application of sensin...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 8, 2023 Category: Neuroscience Source Type: research

A Self-Aligning Upper-Limb Exoskeleton Preserving Natural Shoulder Movements: Kinematic Compatibility Analysis
NESM- $\gamma $ is an upper-limb exoskeleton to train motor functions of post-stroke patients. Based on the kinesiology of the upper limb, the NESM- $\gamma $ includes a four degrees-of-freedom (DOF) active kinematic chain for the shoulder and elbow, along with a passive chain for self-aligning robotic joint axes with the glenohumeral (GH) joint’s center of rotation. The passive chain accounts for scapulohumeral rhythm and trunk rotations. To assess self-aligning performance, we analyzed the kinematic and electromyographic data of the shoulder in eight healthy subjects performing reaching tasks under three experimental c...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 8, 2023 Category: Neuroscience Source Type: research

Highly Anthropomorphic Finger Design With a Novel Friction Clutch for Achieving Human-Like Reach-and-Grasp Movements
In the design of prosthetic hand fingers, achieving human-like movement while meeting anthropomorphic demands such as appearance, size, and lightweight is quite challenging. Human finger movement involves two distinct motion characters during natural reach-and-grasp tasks: consistency in the reaching stage and adaptability in the grasping stage. The former one enhances grasp stability and reduces control complexity; the latter one promotes the adaptability of finger to various objects. However, conventional tendon-driven prosthetic finger designs typically incorporate bulky actuation modules or complex tendon routes to rec...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 7, 2023 Category: Neuroscience Source Type: research

Self-Regulation Phenomenon Emerged During Prolonged Fatigue Driving: An EEG Connectivity Study
In this study, a 90-min simulated driving task was performed on 26 healthy university students. EEG data and reaction time (RT) were synchronously recorded during the whole task. To identify the FSR phenomenon, a data-driven criterion was proposed based on clustering analysis of individual behavioral data and the FSR group was determined as having non-monotonic increase trend of RT and the drops of RT during prolonged driving were more than two levels among the total five levels. The subjects were then divided into two groups: the FSR group and the non-FSR group. Quantitative comparative analysis showed significant differe...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 7, 2023 Category: Neuroscience Source Type: research

A Passive Polycentric Mechanism to Improve Active Mediolateral Balance in Prosthetic Walking
Prosthetic legs are typically passive systems without active ankle control, restricting mediolateral balancing to a hip strategy. Resulting balance control impairments for persons with a lower extremity amputation may be mitigated by increasing hip strategy effectiveness, in which relatively small hip moments of force are adequate for mediolateral balancing. To increase hip strategy effectiveness we have developed a prosthetic leg prototype based on the Peaucellier mechanism, the Sideways Balance Mechanism (SBM). This polycentric mechanism adds a frontal plane degree of freedom, reducing mediolateral body displacements. Ad...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 5, 2023 Category: Neuroscience Source Type: research

MEANSP: How Many Channels are Needed to Predict the Performance of a SMR-Based BCI?
Predicting whether a particular individual would reach an adequate control of a Brain-Computer Interface (BCI) has many practical advantages. On the one hand, participants with low predicted performance could be trained with specifically designed sessions and avoid frustrating experiments; on the other hand, planning time and resources would be more efficient; and finally, the variables related to an accurate prediction could be manipulated to improve the prospective BCI performance. To this end, several predictors have been proposed in the literature, most of them based on the power estimation of EEG signals at the specif...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 5, 2023 Category: Neuroscience Source Type: research

Three-Stream Convolutional Neural Network for Depression Detection With Ocular Imaging
Depression is a prevalent and severe mental disorder that significantly affects both mind and body, leading to persistent feelings of sadness, despair, and impaired functionality. Diagnosis of depression primarily relies on clinical assessment and observation of symptoms. However, due to the lack of objective indicators, the experience and skills of doctor may lead to misdiagnosis. Current researches indicate that eye movement patterns and pupil dilation can serve as potential biomarkers for emotional and cognitive dysregulation in individuals with depression. However, most studies are based on manually extracted eye movem...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 5, 2023 Category: Neuroscience Source Type: research