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.

Epileptic Seizure Detection Based on Path Signature and Bi-LSTM Network With Attention Mechanism
Automatic seizure detection using electroen-cephalogram (EEG) can significantly expedite the diagnosis of epilepsy, thereby facilitating prompt treatment and reducing the risk of future seizures and associated complications. While most existing EEG-based epilepsy detection studies employ deep learning models, they often ignore the chronological relationships between different EEG channels. To tackle this limitation, a novel automatic epilepsy detection method is proposed, which leverages path signature and Bidirectional Long Short-Term Memory (Bi-LSTM) neural network with an attention mechanism. The path signature algorith...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 5, 2024 Category: Neuroscience Source Type: research

3D Knee and Hip Angle Estimation With Reduced Wearable IMUs via Transfer Learning During Yoga, Golf, Swimming, Badminton, and Dance
Wearable lower-limb joint angle estimation using a reduced inertial measurement unit (IMU) sensor set could enable quick, economical sports injury risk assessment and motion capture; however the vast majority of existing research requires a full IMU set attached to every related body segment and is implemented in only a single movement, typically walking. We thus implemented 3-dimensional knee and hip angle estimation with a reduced IMU sensor set during yoga, golf, swimming (simulated lower body swimming in a seated posture), badminton, and dance movements. Additionally, current deep-learning models undergo an accuracy dr...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 4, 2024 Category: Neuroscience Source Type: research

Trajectory Deformation-Based Multi-Modal Adaptive Compliance Control for a Wearable Lower Limb Rehabilitation Robot
This article presents a trajectory deformation-based multi-modal adaptive compliance control strategy (TD-MACCS) for a wearable lower limb rehabilitation robot (WLLRR), which includes a high-level trajectory planner and a low-level position controller. Dynamic motion primitives (DMPs) and a trajectory deformation algorithm (TDA) are integrated into the high-level trajectory planner, generating multi-joint synchronized desired trajectories through physical human-robot interaction (pHRI). In particular, the amplitude modulation factor of DMPs and the deformation factor of TDA are adapted by a multi-modal adaptive regulator, ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 2, 2024 Category: Neuroscience Source Type: research

Effect Analysis of Wearing an Lumbar Exoskeleton on Coordinated Activities of the Low Back Muscles Using sEMG Topographic Maps
This study used the surface electromyography (sEMG) topographic map based on multi-channel electrodes from low back muscles to analyze the effects. Thirteen subjects conducted two tasks, namely lifting and holding a 20kg-weight box. For each task, three different trials, not wearing exoskeleton (NoExo), wearing exoskeleton but power-off (OffExo), and wearing exoskeleton and power-on (OnExo), were randomly conducted. Root-mean-square (RMS) and median-frequency (MDF) topographic maps of the recorded sEMG were constructed. Three parameters, average pixel values, distribution of center of gravity (CoG), and entropy, were extra...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 2, 2024 Category: Neuroscience Source Type: research

Self-Balancing Exoskeleton Robots Designed to Facilitate Multiple Rehabilitation Training Movements
This study presents the biomimetic design of the structure and controller of AutoLEE-II, a self-balancing exoskeleton developed to assist patients in performing multiple rehabilitation movements without crutches or other supporting equipment. Its structural design is founded upon the human body structure, with an eliminated axis deviation and a raised CoM of the exoskeleton. The controller is a physical parameter-independent controller based on the CoM modification. Thus, the exoskeleton can adapt to patients with different physical parameters. Five subjects underwent exoskeleton-assisted rehabilitation training experiment...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2024 Category: Neuroscience Source Type: research

Decoding Bilingual EEG Signals With Complex Semantics Using Adaptive Graph Attention Convolutional Network
Decoding neural signals of silent reading with Brain-Computer Interface (BCI) techniques presents a fast and intuitive communication method for severely aphasia patients. Electroencephalogram (EEG) acquisition is convenient and easily wearable with high temporal resolution. However, existing EEG-based decoding units primarily concentrate on individual words due to their low signal-to-noise ratio, rendering them insufficient for facilitating daily communication. Decoding at the word level is less efficient than decoding at the phrase or sentence level. Furthermore, with the popularity of multilingualism, decoding EEG signal...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2024 Category: Neuroscience Source Type: research

Rehabilitation Evaluation of Upper Limb Motor Function for Stroke Patients Based on Belief Rule Base
In the process of rehabilitation treatment for stroke patients, rehabilitation evaluation is a significant part in rehabilitation medicine. Researchers intellectualized the evaluation of rehabilitation evaluation methods and proposed quantitative evaluation methods based on evaluation scales, without the clinical background of physiatrist. However, in clinical practice, the experience of physiatrist plays an important role in the rehabilitation evaluation of patients. Therefore, this paper designs a 5 degrees of freedom (DoFs) upper limb (UL) rehabilitation robot and proposes a rehabilitation evaluation model based on Beli...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 28, 2023 Category: Neuroscience Source Type: research

An IMU-Based Ground Reaction Force Estimation Method and Its Application in Walking Balance Assessment
Walking is one of the most common daily movements of the human body. Therefore, quantitative evaluation of human walking has been commonly used to assist doctors in grasping the disease degree and rehabilitation process of patients in the clinic. Compared with the kinematic characteristics, the ground reaction force (GRF) during walking can directly reflect the dynamic characteristics of human walking. It can further help doctors understand the degree of muscle recovery and joint coordination of patients. This paper proposes a GRF estimation method based on the elastic elements and Newton-Euler equation hybrid driving GRF ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 28, 2023 Category: Neuroscience Source Type: research

Enhancing EEG and sEMG Fusion Decoding Using a Multi-Scale Parallel Convolutional Network With Attention Mechanism
This study indicates that using this model to fuse EEG and sEMG signals can improve the accuracy and stability of hand rehabilitation training for patients. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 26, 2023 Category: Neuroscience Source Type: research

Navigation Learning Assessment Using EEG-Based Multi-Time Scale Spatiotemporal Compound Model
This study presents a novel method to assess the learning effectiveness using Electroencephalography (EEG)-based deep learning model. It is difficult to assess the learning effectiveness of professional courses in cultivating students’ ability objectively by questionnaire or other assessment methods. Research in the field of brain has shown that innovation ability can be reflected from cognitive ability which can be embodied by EEG signal features. Three navigation tasks of increasing cognitive difficulty were designed and a total of 41 subjects participated in the experiment. For the classification and tracking of the s...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 25, 2023 Category: Neuroscience Source Type: research

Electrical Stimulation of Regenerative Peripheral Nerve Interfaces (RPNIs) Induces Referred Sensations in People With Upper Limb Loss
Individuals with upper limb loss lack sensation of the missing hand, which can negatively impact their daily function. Several groups have attempted to restore this sensation through electrical stimulation of residual nerves. The purpose of this study was to explore the utility of regenerative peripheral nerve interfaces (RPNIs) in eliciting referred sensation. In four participants with upper limb loss, we characterized the quality and location of sensation elicited through electrical stimulation of RPNIs over time. We also measured functional stimulation ranges (sensory perception and discomfort thresholds), sensitivity t...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 25, 2023 Category: Neuroscience Source Type: research

Impact of Network Topology on Neural Synchrony in a Model of the Subthalamic Nucleus-Globus Pallidus Circuit
This study investigates the relationship between network structure and neural synchrony in a model of the STN-GPe circuit comprised of conductance-based spiking neurons. Changes in net synaptic input were controlled for through a synaptic scaling rule, which facilitated separation of the effects of network structure from net synaptic input. Five topologies were examined as structures for the STN-GPe circuit: Watts-Strogatz, preferential attachment, spatial, stochastic block, k-regular random. Beta band synchrony generally increased as the number of connections increased, however the exact relationship was topology specific...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 25, 2023 Category: Neuroscience Source Type: research

Regional-Asymmetric Adaptive Graph Convolutional Neural Network for Diagnosis of Autism in Children With Resting-State EEG
Currently, resting-state electroencephalography (rs-EEG) has become an effective and low-cost evaluation way to identify autism spectrum disorders (ASD) in children. However, it is of great challenge to extract useful features from raw rs-EEG data to improve diagnosis performance. Traditional methods mainly rely on the design of manual feature extractors and classifiers, which are separately performed and cannot be optimized simultaneously. To this end, this paper proposes a new end-to-end diagnostic method based on a recently emerged graph convolutional neural network for the diagnosis of ASD in children. Inspired by rela...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 25, 2023 Category: Neuroscience Source Type: research

Exploring Frequency Band-Based Biomarkers of EEG Signals for Mild Cognitive Impairment Detection
Mild Cognitive Impairment (MCI) is often considered a precursor to Alzheimer’s disease (AD), with a high likelihood of progression. Accurate and timely diagnosis of MCI is essential for halting the progression of AD and other forms of dementia. Electroencephalography (EEG) is the prevalent method for identifying MCI biomarkers. Frequency band-based EEG biomarkers are crucial for identifying MCI as they capture neuronal activities and connectivity patterns linked to cognitive functions. However, traditional approaches struggle to identify precise frequency band-based biomarkers for MCI diagnosis. To address this challenge...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 25, 2023 Category: Neuroscience Source Type: research

B2-ViT Net: Broad Vision Transformer Network With Broad Attention for Seizure Prediction
Seizure prediction are necessary for epileptic patients. The global spatial interactions among channels, and long-range temporal dependencies play a crucial role in seizure onset prediction. In addition, it is necessary to search for seizure prediction features in a vast space to learn new generalized feature representations. Many previous deep learning algorithms have achieved some results in automatic seizure prediction. However, most of them do not consider global spatial interactions among channels and long-range temporal dependencies together, and only learn the feature representation in the deep space. To tackle thes...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 25, 2023 Category: Neuroscience Source Type: research