IEE Transactions on Neural Systems and Rehabilitation Engineering 
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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.The Fundamental Property of Human Leg During Walking: Linearity and Nonlinearity
This study presented that the fundamental leg properties during human walking comprise axial stiffness, rest leg length, tangential stiffness and force-free leg angles. We measured the axial force-leg length and tangential force-leg angle data in eight participants (mean ± s.d. age 24.6 ± 3.0 years, mass 68.2 ± 6.8 kg, height 177.5 ± 5.2 cm) at three self-selected walking speeds (slow: 1.25 ± 0.22, normal: 1.48 ± 0.28, fast: 1.75 ± 0.32 m/s) on two different contact conditions (fixed and moving). After obtaining these gait measurements, we extracted the linear and nonlinear leg elasticities during human walking by u...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 5, 2023 Category: Neuroscience Source Type: research
Ventral and Dorsal Stream EEG Channels: Key Features for EEG-Based Object Recognition and Identification
Object recognition and object identification are multifaceted cognitive operations that require various brain regions to synthesize and process information. Prior research has evidenced the activity of both visual and temporal cortices during these tasks. Notwithstanding their similarities, object recognition and identification are recognized as separate brain functions. Drawing from the two-stream hypothesis, our investigation aims to understand whether the channels within the ventral and dorsal streams contain pertinent information for effective model learning regarding object recognition and identification tasks. By uti...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 5, 2023 Category: Neuroscience Source Type: research
Deep Unsupervised Representation Learning for Feature-Informed EEG Domain Extraction
This study proposes a novel inference model, the Joint Embedding Variational Autoencoder, that offers conditionally tighter approximation of the estimated spatiotemporal feature distribution through the use of jointly optimised variational autoencoders to achieve optimizable data dependent inputs as an additional variable for improved overall model optimisation and scaling without sacrificing model tightness. To learn the variational bound, we show that maximising the marginal log-likelihood of only the second embedding section is required to achieve conditionally tighter lower bounds. Furthermore, we show that this model ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 4, 2023 Category: Neuroscience Source Type: research
Magnetorheological Damper With Variable Displacement Permanent Magnet for Assisting the Transfer of Load in Lower Limb Exoskeleton
Magnetorheological (MR) fluid exhibits the ability to modulate its shear state through variations in magnetic field intensity, and is widely used for applications requiring damping. Traditional MR dampers use the current in the coil to adjust the magnetic field strength, but the accumulated heat can cause the magnetic field strength to decay if it works for a long time. In order to deal with this shortcoming, a novel MR damper is proposed in this paper, which is based on a variable displacement permanent magnet to adjust the output resistance torque and applied to an exoskeleton joint for human load transfer assistance. A ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 1, 2023 Category: Neuroscience Source Type: research
A Novel Low-Pressure Robotic Glove Based on CT-Optimized Finger Joint Kinematic Model for Long-Term Rehabilitation of Stroke Patients
Wearing robotic gloves has become increasingly crucial for hand rehabilitation in stroke patients. However, traditional robotic gloves can exert additional pressure on the hand, such as prolonged use leading to poor blood circulation and muscle stiffness. To address these concerns, this work analyzes the finger kinematic model based on computerized tomography (CT) images of human hands, and designs a low-pressure robotic glove that conforms to finger kinematic characteristics. Firstly, physiological data on finger joint flexion and extension were collected through CT scans. The equivalent rotation centers of finger joints ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 30, 2023 Category: Neuroscience Source Type: research
SFDA: Domain Adaptation With Source Subject Fusion Based on Multi-Source and Single-Target Fall Risk Assessment
This study proves the effectiveness of SFDA and provides a novel tool for implementing cross-subject and few-gait fall risk assessment. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 30, 2023 Category: Neuroscience Source Type: research
Alterations of Motor Unit Characteristics Associated With Muscle Fatigue
This study aims to characterize motor unit (MU) features associated with muscle fatigue, using high-density surface electromyography (HD-sEMG). The same MUs recruited before/after, and during muscle fatigue were identified for analysis. The surface location of the innervation zones (IZs) of the MUs was identified from the HD-sEMG bipolar motor unit action potential (MUAP) map. The depth of the MU was also identified from the decay pattern of the MUAP along the muscle fiber transverse direction. Both the surface IZ location and the MU depth information were utilized to ensure the same MU was examined during the contraction ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 30, 2023 Category: Neuroscience Source Type: research
Accurate COP Trajectory Estimation in Healthy and Pathological Gait Using Multimodal Instrumented Insoles and Deep Learning Models
This study introduces novel deep recurrent neural networks that can accurately estimate dynamic COP trajectories by fusing data from affordable and heterogeneous insole-embedded sensors (namely, an eight-cell array of force sensitive resistors (FSRs) and an inertial measurement unit (IMU)). The method was validated against gold-standard equipment during out-of-the-lab ambulatory tasks that simulated real-world walking. Root-mean-square errors (RMSE) in the mediolateral (ML) and anteroposterior (AP) directions obtained from healthy individuals (ML: 0.51 cm, AP: 1.44 cm) and individuals with neuromuscular conditions (ML: 0.5...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 30, 2023 Category: Neuroscience Source Type: research
Effectiveness of Repetitive Transcranial Magnetic Stimulation Combined With Transspinal Electrical Stimulation on Corticospinal Excitability for Individuals With Incomplete Spinal Cord Injury: A Pilot Study
In conclusion, the effectiveness of 8-week combined therapy in increasing corticospinal excitability faded 4 weeks after the cessation of treatment. According to the results, combination of iTBS rTMS and tsDCS treatment was more effective than was iTBS rTMS alone or tsDCS alone in enhancing corticospinal excitability. Although promising, the results of this study must be validated by studies with longer interventions and larger sample sizes. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 30, 2023 Category: Neuroscience Source Type: research
Mixture of Experts for EEG-Based Seizure Subtype Classification
Epilepsy is a pervasive neurological disorder affecting approximately 50 million individuals worldwide. Electroencephalogram (EEG) based seizure subtype classification plays a crucial role in epilepsy diagnosis and treatment. However, automatic seizure subtype classification faces at least two challenges: 1) class imbalance, i.e., certain seizure types are considerably less common than others; and 2) no a priori knowledge integration, so that a large number of labeled EEG samples are needed to train a machine learning model, particularly, deep learning. This paper proposes two novel Mixture of Experts (MoE) models, Seizure...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 30, 2023 Category: Neuroscience Source Type: research
Relevance of Upper Limb Muscle Synergies to Dynamic Force Generation: Perspectives on Rehabilitation of Impaired Intermuscular Coordination in Stroke
This study investigated the impact of stroke on the control of upper limb endpoint force during isokinetic exercise, a dynamic force-generating task, and its association with stroke-affected muscle synergies. Three-dimensional upper limb endpoint force and electromyography of shoulder and elbow muscles were collected from sixteen chronic stroke survivors and eight neurologically intact adults. Participants were instructed to control the endpoint force direction during three-dimensional isokinetic upper limb movements. The endpoint force control performance was quantitatively evaluated in terms of the coupling between force...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 28, 2023 Category: Neuroscience Source Type: research
Ankle Torque Estimation With Motor Unit Discharges in Residual Muscles Following Lower-Limb Amputation
This study aims to investigate the potential of using motor unit (MU)-based decoding methods as an alternative to EMG-based intent recognition for ankle torque estimation. Eight people without amputation (NON) and seven people with amputation (AMP) participated in the experiments. Subjects conducted isometric dorsi- and plantarflexion with their intact limb by tracing desired muscle activity of the tibialis anterior (TA) and gastrocnemius (GA) while ankle torque was recorded. To match phantom limb and intact limb activity, AMP mirrored muscle activation with their residual TA and GA. We compared neuromuscular decoders (lin...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 28, 2023 Category: Neuroscience Source Type: research
Graph Reasoning Module for Alzheimer’s Disease Diagnosis: A Plug-and-Play Method
Recent advances in deep learning have led to increased adoption of convolutional neural networks (CNN) for structural magnetic resonance imaging (sMRI)-based Alzheimer’s disease (AD) detection. AD results in widespread damage to neurons in different brain regions and destroys their connections. However, current CNN-based methods struggle to relate spatially distant information effectively. To solve this problem, we propose a graph reasoning module (GRM), which can be directly incorporated into CNN-based AD detection models to simulate the underlying relationship between different brain regions and boost AD diagnosis perf...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 28, 2023 Category: Neuroscience Source Type: research
A Novel Hybrid Brain–Computer Interface Combining the Illusion-Induced VEP and SSVEP
In this study, we designed and implemented a novel hybrid paradigm that combined illusion-induced visual evoked potential (IVEP) and steady-state visual evoked potential (SSVEP) with the aim of leveraging their features simultaneously to improve system efficiency. The proposed paradigm was validated through two experimental studies, which encompassed feature analysis of IVEP with a static paradigm, and performance evaluation of hybrid paradigm in comparison with the conventional SSVEP paradigm. The characteristic analysis yielded significant differences in response waveforms among different motion illusions. The performanc...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 28, 2023 Category: Neuroscience Source Type: research
LSTM-MSA: A Novel Deep Learning Model With Dual-Stage Attention Mechanisms Forearm EMG-Based Hand Gesture Recognition
This paper introduces the Long Short-Term Memory with Dual-Stage Attention (LSTM-MSA) model, an approach for analyzing electromyography (EMG) signals. EMG signals are crucial in applications like prosthetic control, rehabilitation, and human-computer interaction, but they come with inherent challenges such as non-stationarity and noise. The LSTM-MSA model addresses these challenges by combining LSTM layers with attention mechanisms to effectively capture relevant signal features and accurately predict intended actions. Notable features of this model include dual-stage attention, end-to-end feature extraction and classifica...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 28, 2023 Category: Neuroscience Source Type: research
