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Prediction of Dexterous Finger Forces with Forearm Rotation using Motoneuron Discharges IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-17 Bofang Zheng, Yixin Li, Guanghua Xu, Gang Wang, Yang Zheng
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Uncovering hemispheric asymmetry and directed oscillatory brain-heart interplay in anxiety processing: an fMRI study IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-15 Ameer Ghouse, Gert Pfurtscheller, Gerhard Schwarz, Gaetano Valenza
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An Expert-Knowledge-Based Graph Convolutional Network for Skeleton- Based Physical Rehabilitation Exercises Assessment IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-14 Tian He, Yang Chen, Ling Wang, Hong Cheng
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Gait Training-Based Motor Imagery and EEG Neurofeedback in Lokomat: A Clinical Intervention With Complete Spinal Cord Injury Individuals IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-13 Ericka Raiane S. Serafini, Cristian D. Guerrero-Mendez, Teodiano F. Bastos-Filho, Anibal Cotrina-Atencio, André F. O. de Azevedo Dantas, Denis Delisle-Rodriguez, Caroline C. do Espírito-Santo
Robotic systems, such as Lokomat® have shown promising results in people with severe motor impairments, who suffered a stroke or other neurological damage. Robotic devices have also been used by people with more challenging damages, such as Spinal Cord Injury (SCI), using feedback strategies that provide information about the brain activity in real-time. This study proposes a novel Motor Imagery (MI)-based
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Cross-Subject Lifelong Learning for Continuous Estimation from Surface Electromyographic Signal IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-13 Xingjian Chen, Weiyu Guo, Chuang Lin, Ning Jiang, Jingyong Su
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Validity and Impact of Methods for Collecting Training Data for Myoelectric Prosthetic Control Algorithms IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-13 Troy N. Tully, Caleb J. Thomson, Gregory A. Clark, Jacob A. George
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s-TBN: A new neural decoding model to identify stimulus categories from brain activity patterns IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-09 Chunyu Liu, Bokai Cao, Jiacai Zhang
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Identification of Optimal and Most Significant Event Related Brain Functional Network IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-09 Venkateswarlu Gonuguntla, A. T. Adebisi, Kalyana C. Veluvolu
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Accuracy of Video-Based Hand Tracking for People With Upper-Body Disabilities IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-09 Alexandra A. Portnova-Fahreeva, Momona Yamagami, Adrià Robert-Gonzalez, Jennifer Mankoff, Heather Feldner, Katherine M. Steele
Utilization of hand-tracking cameras, such as Leap, for hand rehabilitation and functional assessments is an innovative approach to providing affordable alternatives for people with disabilities. However, prior to deploying these commercially-available tools, a thorough evaluation of their performance for disabled populations is necessary. In this study, we provide an in-depth analysis of the accuracy
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BiLSTM-based joint torque prediction from mechanomyogram during isometric contractions: A proof of concept study IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-09 Jongsang Son, Fandi Shi, William Zev Rymer
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TASA: Temporal Attention with Spatial Autoencoder Network for Odor-induced Emotion Classification Using EEG IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-09 Chengxuan Tong, Yi Ding, Zhuo Zhang, Haihong Zhang, Kevin JunLiang Lim, Cuntai Guan
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Development of a motion-based video game for postural training: a feasibility study on older adults with adult degenerative scoliosis IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-08 Frances K. W. Wan, Alex T. H. Mak, Claire W. Y. Chung, Joanne Y. W. Yip
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DMA-HPCNet: Dual Multi-level Attention Hybrid Pyramid Convolution Neural Network for Alzheimer’s Disease Classification# IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-08 Shiguan Mu, Shixiao Shan, Lanlan Li, Shuiqing Jing, Ruohan Li, Chunhou Zheng, Xinchun Cui
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Neurovascular Coupling Analysis Based on Multivariate Variational Gaussian Process Convergent Cross-Mapping IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-08 Renfei Zhu, Qingshan She, Rihui Li, Tongcai Tan, Yingchun Zhang
Neurovascular coupling (NVC) provides important insights into the intricate activity of brain functioning and may aid in the early diagnosis of brain diseases. Emerging evidences have shown that NVC could be assessed by the coupling between electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). However, this endeavor presents significant challenges due to the absence of standardized
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2CFastICA: A N ovel Method for H igh D ensity S urface EMG D ecomposition B ased on K ernel C onstrained FastICA and C orrelation C onstrained FastICA IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-08 Maoqi Chen, Ping Zhou
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Preliminary Study on Effects of Neck Exoskeleton Structural Design in Patients With Amyotrophic Lateral Sclerosis IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-06 David Demaree, Joseph Brignone, Mark Bromberg, Haohan Zhang
Neck muscle weakness due to amyotrophic lateral sclerosis (ALS) can result in dropped head syndrome, adversely impacting the quality of life of those affected. Static neck collars are currently prescribed to hold the head in a fixed upright position. However, these braces are uncomfortable and do not allow any voluntary head-neck movements. By contrast, powered neck exoskeletons have the potential
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Effectiveness of Intelligent Control Strategies in Robot-Assisted Rehabilitation—A Systematic Review IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-02 Dexter Felix Brown, Sheng Quan Xie
This review aims to provide a systematic analysis of the literature focused on the use of intelligent control systems in robotics for physical rehabilitation, identifying trends in recent research and comparing the effectiveness of intelligence used in control, with the aim of determining important factors in robot-assisted rehabilitation and how intelligent controller design can improve them. Seven
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Association Between Sleep Quality and Deep Learning-Based Sleep Onset Latency Distribution Using an Electroencephalogram IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-02 Seungwon Oh, Young-Seok Kweon, Gi-Hwan Shin, Seong-Whan Lee
To evaluate sleep quality, it is necessary to monitor overnight sleep duration. However, sleep monitoring typically requires more than 7 hours, which can be inefficient in termxs of data size and analysis. Therefore, we proposed to develop a deep learning-based model using a 30 sec sleep electroencephalogram (EEG) early in the sleep cycle to predict sleep onset latency (SOL) distribution and explore
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Study on Improving the Modulatory Effect of Rhythmic Oscillations by Transcranial Magneto-Acoustic Stimulation IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-05-01 Ruxin Tan, Ren Ma, Fangxuan Chu, Xiaoqing Zhou, Xin Wang, Tao Yin, Zhipeng Liu
In hippocampus, synaptic plasticity and rhythmic oscillations reflect the cytological basis and the intermediate level of cognition, respectively. Transcranial ultrasound stimulation (TUS) has demonstrated the ability to elicit changes in neural response. However, the modulatory effect of TUS on synaptic plasticity and rhythmic oscillations was insufficient in the present studies, which may be attributed
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An Improved Extreme Learning Machine (ELM) Algorithm for Intent Recognition of Transfemoral Amputees With Powered Knee Prosthesis IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-29 Yao Zhang, Xu Wang, Haohua Xiu, Wei Chen, Yongxin Ma, Guowu Wei, Lei Ren, Luquan Ren
To overcome the challenges posed by the complex structure and large parameter requirements of existing classification models, the authors propose an improved extreme learning machine (ELM) classifier for human locomotion intent recognition in this study, resulting in enhanced classification accuracy. The structure of the ELM algorithm is enhanced using the logistic regression (LR) algorithm, significantly
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Multi-Task Heterogeneous Ensemble Learning-Based Cross-Subject EEG Classification Under Stroke Patients IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-29 Minji Lee, Hyeong-Yeong Park, Wanjoo Park, Keun-Tae Kim, Yun-Hee Kim, Ji-Hoon Jeong
Robot-assisted motor training is applied for neurorehabilitation in stroke patients, using motor imagery (MI) as a representative paradigm of brain-computer interfaces to offer real-life assistance to individuals facing movement challenges. However, the effectiveness of training with MI may vary depending on the location of the stroke lesion, which should be considered. This paper introduces a multi-task
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Extracting Stress-Related EEG Patterns From Pre-Sleep EEG for Forecasting Slow-Wave Sleep Deficiency IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-29 Cheng-Hua Su, Li-Wei Ko, Tzyy-Ping Jung, Julie Onton, Shey-Cherng Tzou, Jia-Chi Juang, Chung-Yao Hsu
Sleep is vital to our daily activity. Lack of proper sleep can impair functionality and overall health. While stress is known for its detrimental impact on sleep quality, the precise effect of pre-sleep stress on subsequent sleep structure remains unknown. This study introduced a novel approach to study the pre-sleep stress effect on sleep structure, specifically slow-wave sleep (SWS) deficiency. To
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Real-Time Precise Targeting of the Subthalamic Nucleus via Transfer Learning in a Rat Model of Parkinson’s Disease Based on Microelectrode Arrays IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-24 Qianli Jia, Luyi Jing, Yuxin Zhu, Meiqi Han, Peiyao Jiao, Yu Wang, Zhaojie Xu, Yiming Duan, Mixia Wang, Xinxia Cai
In neurodegenerative disorders, neuronal firing patterns and oscillatory activity are remarkably altered in specific brain regions, which can serve as valuable biomarkers for the identification of deep brain regions. The subthalamic nucleus (STN) has been the primary target for DBS in patients with Parkinson’s disease (PD). In this study, changes in the spike firing patterns and spectral power of local
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Exploration and Application of a Muscle Fatigue Assessment Model Based on NMF for Multi-Muscle Synergistic Movements IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-24 Jinxu Yu, Lijie Zhang, Yihao Du, Xiaoran Wang, Jianhua Yan, Jie Chen, Ping Xie
Muscle fatigue significantly impacts coordination, stability, and speed in daily activities. Accurate assessment of muscle fatigue is vital for effective exercise programs, injury prevention, and sports performance enhancement. Current methods mostly focus on individual muscles and strength evaluation, overlooking overall fatigue in multi-muscle movements. This study introduces a comprehensive muscle
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Illusory Directional Sensation Induced by Asymmetric Vibrations Influences Sense of Agency and Velocity in Wrist Motions IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-24 Takeshi Tanabe, Hidekazu Kaneko
Illusory directional sensations are generated through asymmetric vibrations applied to the fingertips and have been utilized to induce upper-limb motions in the rehabilitation and training of patients with visual impairment. However, its effects on motor control remain unclear. This study aimed to verify the effects of illusory directional sensations on wrist motion. We conducted objective and subjective
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Resist-as-Needed ADL Training With SPINDLE for Patients With Tremor IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-23 Nikhil Tej Kantu, Ryan Osswald, Amit Kandel, Jiyeon Kang
Individuals with neurological disorders often exhibit altered manual dexterity and muscle weakness in their upper limbs. These motor impairments with tremor lead to severe difficulties in performing Activities of Daily Living (ADL). There is a critical need for ADL-focused robotic training that improves individual’s strength when engaging with dexterous ADL tasks. This research introduces a new approach
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BlazePose-Seq2Seq: Leveraging Regular RGB Cameras for Robust Gait Assessment IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-22 Abdul Aziz Hulleck, Aamna AlShehhi, Marwan El Rich, Raviha Khan, Rateb Katmah, Mahdi Mohseni, Navid Arjmand, Kinda Khalaf
Evaluation of human gait through smartphone-based pose estimation algorithms provides an attractive alternative to costly lab-bound instrumented assessment and offers a paradigm shift with real time gait capture for clinical assessment. Systems based on smart phones, such as OpenPose and BlazePose have demonstrated potential for virtual motion assessment but still lack the accuracy and repeatability
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E-BabyNet: Enhanced Action Recognition of Infant Reaching in Unconstrained Environments IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-22 Amel Dechemi, Konstantinos Karydis
Machine vision and artificial intelligence hold promise across healthcare applications. In this paper, we focus on the emerging research direction of infant action recognition, and we specifically consider the task of reaching which is an important developmental milestone. We develop E-babyNet, a lightweight yet effective neural-network-based framework for infant action recognition that leverages the
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Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-22 Xiaoqing Chen, Ziwei Wang, Dongrui Wu
Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding accuracy, with only a few considering the adversarial security. Although many adversarial defense approaches have been proposed in other application domains such as computer vision, previous research showed that their direct extensions
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A Subject-Specific Attention Index Based on the Weighted Spectral Power IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-22 Guiying Xu, Zhenyu Wang, Xi Zhao, Ruxue Li, Ting Zhou, Tianheng Xu, Honglin Hu
As an essential cognitive function, attention has been widely studied and various indices based on EEG have been proposed for its convenience and easy availability for real-time attention monitoring. Although existing indices based on spectral power of empirical frequency bands are able to describe the attentional state in some way, the reliability still needs to be improved. This paper proposed a
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Simplifying Multimodal With Single EOG Modality for Automatic Sleep Staging IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-18 Yangxuan Zhou, Sha Zhao, Jiquan Wang, Haiteng Jiang, Zhenghe Yu, Shijian Li, Tao Li, Gang Pan
Polysomnography (PSG) recordings have been widely used for sleep staging in clinics, containing multiple modality signals (i.e., EEG and EOG). Recently, many studies have combined EEG and EOG modalities for sleep staging, since they are the most and the second most powerful modality for sleep staging among PSG recordings, respectively. However, EEG is complex to collect and sensitive to environment
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A Siamese Convolutional Neural Network for Identifying Mild Traumatic Brain Injury and Predicting Recovery IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-18 Fatemeh Koochaki, Laleh Najafizadeh
Timely diagnosis of mild traumatic brain injury (mTBI) remains challenging due to the rapid recovery of acute symptoms and the absence of evidence of injury in static neuroimaging scans. Furthermore, while longitudinal tracking of mTBI is essential in understanding how the diseases progresses/regresses over time for enhancing personalized patient care, a standardized approach for this purpose is not
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Automatic Detection of Scalp High-Frequency Oscillations Based on Deep Learning IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-16 Yutang Li, Dezhi Cao, Junda Qu, Wei Wang, Xinhui Xu, Lingyu Kong, Jianxiang Liao, Wenhan Hu, Kai Zhang, Jihan Wang, Chunlin Li, Xiaofeng Yang, Xu Zhang
Scalp high-frequency oscillations (sHFOs) are a promising non-invasive biomarker of epilepsy. However, the visual marking of sHFOs is a time-consuming and subjective process, existing automatic detectors based on single-dimensional analysis have difficulty with accurately eliminating artifacts and thus do not provide sufficient reliability to meet clinical needs. Therefore, we propose a high-performance
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Improving SSVEP-BCI Performance Through Repetitive Anodal tDCS-Based Neuromodulation: Insights From Fractal EEG and Brain Functional Connectivity IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-16 Shangen Zhang, Hongyan Cui, Yong Li, Xiaogang Chen, Xiaorong Gao, Cuntai Guan
This study embarks on a comprehensive investigation of the effectiveness of repetitive transcranial direct current stimulation (tDCS)-based neuromodulation in augmenting steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs), alongside exploring pertinent electroencephalography (EEG) biomarkers for assessing brain states and evaluating tDCS efficacy. EEG data were garnered across
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Muscle Synergy Plasticity in Motor Function Recovery After Stroke IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-15 Yixuan Sheng, Jixian Wang, Gansheng Tan, Hui Chang, Qing Xie, Honghai Liu
In certain neurological disorders such as stroke, the impairment of upper limb function significantly impacts daily life quality and necessitates enhanced neurological control. This poses a formidable challenge in the realm of rehabilitation due to its intricate nature. Moreover, the plasticity of muscle synergy proves advantageous in assessing the enhancement of motor function among stroke patients
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Multi-Scale Masked Autoencoders for Cross-Session Emotion Recognition IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-15 Miaoqi Pang, Hongtao Wang, Jiayang Huang, Chi-Man Vong, Zhiqiang Zeng, Chuangquan Chen
Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion recognition. However, the time-consuming process of annotating EEG data, inherent individual differences, non-stationary characteristics of EEG data, and noise artifacts in EEG data collection pose formidable challenges in developing
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Socially Assistive Robot for Stroke Rehabilitation: A Long-Term in-the-Wild Pilot Randomized Controlled Trial IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-10 Ronit Feingold-Polak, Oren Barzel, Shelly Levy-Tzedek
Socially assistive robots (SARs) have been suggested as a platform for post-stroke training. It is not yet known whether long-term interaction with a SAR can lead to an improvement in the functional ability of individuals post-stroke. The aim of this pilot study was to compare the changes in motor ability and quality of life following a long-term intervention for upper-limb rehabilitation of post-stroke
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A Time-Local Weighted Transformation Recognition Framework for Steady State Visual Evoked Potentials Based Brain–Computer Interfaces IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-10 Ke Qin, Ren Xu, Shurui Li, Xingyu Wang, Andrzej Cichocki, Jing Jin
Canonical correlation analysis (CCA), Multivariate synchronization index (MSI), and their extended methods have been widely used for target recognition in Brain-computer interfaces (BCIs) based on Steady State Visual Evoked Potentials (SSVEP), and covariance calculation is an important process for these algorithms. Some studies have proved that embedding time-local information into the covariance can
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Multi-Stimulus Least-Squares Transformation With Online Adaptation Scheme to Reduce Calibration Effort for SSVEP-Based BCIs IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-10 Dandan Li, Xuedong Wang, Mingliang Dou, Yao Zhao, Xiaohong Cui, Jie Xiang, Bin Wang
Steady-state visual evoked potential (SSVEP), one of the most popular electroencephalography (EEG)-based brain-computer interface (BCI) paradigms, can achieve high performance using calibration-based recognition algorithms. As calibration-based recognition algorithms are time-consuming to collect calibration data, the least-squares transformation (LST) has been used to reduce the calibration effort
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Explainable Deep-Learning Prediction for Brain–Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-05 Ping-Ju Lin, Wei Li, Xiaoxue Zhai, Zhibin Li, Jingyao Sun, Quan Xu, Yu Pan, Linhong Ji, Chong Li
Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state
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Wearable Motion Analysis System for Thoracic Spine Mobility With Inertial Sensors IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-04 Chenyao Zhu, Lan Luo, Rui Li, Junhui Guo, Qining Wang
This study presents a wireless wearable portable system designed for the automatic quantitative spatio-temporal analysis of continuous thoracic spine motion across various planes and degrees of freedom (DOF). This includes automatic motion segmentation, computation of the range of motion (ROM) for six distinct thoracic spine movements across three planes, tracking of motion completion cycles, and visualization
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A Deep Quantum Convolutional Neural Network Based Facial Expression Recognition For Mental Health Analysis IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-04 Sanoar Hossain, Saiyed Umer, Ranjeet Kumar Rout, Hasan Al Marzouqi
The purpose of this work is to analyze how new technologies can enhance clinical practice while also examining the physical traits of emotional expressiveness of face expression in a number of psychiatric illnesses. Hence, in this work, an automatic facial expression recognition system has been proposed that analyzes static, sequential, or video facial images from medical healthcare data to detect
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A Multi-modal Classification Method for Early Diagnosis of Mild Cognitive Impairment and Alzheimer’s Disease Using Three Paradigms with Various Task Difficulties IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-03 Sheng Chen, Chutian Zhang, Hongjun Yang, Liang Peng, Haiqun Xie, Zeping Lv, Zeng-Guang Hou
Alzheimer’s Disease (AD) accounts for the majority of dementia, and Mild Cognitive Impairment (MCI) is the early stage of AD. Early and accurate diagnosis of dementia plays a vital role in more targeted treatments and effectively halting disease progression. However, the clinical diagnosis of dementia requires various examinations, which are expensive and require a high level of expertise from the
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VSSI-GGD: A Variation Sparse EEG Source Imaging Approach Based on Generalized Gaussian Distribution IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-02 Ke Liu, Shu Peng, Chengzhi Liang, Zhuliang Yu, Bin Xiao, Guoyin Wang, Wei Wu
Electroencephalographic (EEG) source imaging (ESI) is a powerful method for studying brain functions and surgical resection of epileptic foci. However, accurately estimating the location and extent of brain sources remains challenging due to noise and background interference in EEG signals. To reconstruct extended brain sources, we propose a new ESI method called Variation Sparse Source Imaging based
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Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-01 Zijun Wei, Zhi-Qiang Zhang, Sheng Quan Xie
Upper limb functional impairments persisting after stroke significantly affect patients’ quality of life. Precise adjustment of robotic assistance levels based on patients’ motion intentions using sEMG signals is crucial for active rehabilitation. This paper systematically reviews studies on continuous prediction of upper limb single joints and multi-joint combinations motion intention using Model-Based
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Willed Attentional Selection of Visual Features: An EEG Study IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-01 Jingyi Wang, Jiaqi Wang, Jingyi Hu, Shanbao Tong, Xiangfei Hong, Junfeng Sun
Visual selective attention studies generally tend to apply cuing paradigms to instructively direct observers’ attention to certain locations, features or objects. However, in real situations, attention in humans often flows spontaneously without any specific instructions. Recently, a concept named “willed attention” was raised in visuospatial attention, in which participants are free to make volitional
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Does Exerting Grasps Involve a Finite Set of Muscle Patterns? A Study of Intra- and Intersubject Variability of Forearm sEMG Signals in Seven Grasp Types IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-29 Néstor J. Jarque-Bou, Margarita Vergara, Joaquín L. Sancho-Bru
Surface Electromyography (sEMG) signals are widely used as input to control robotic devices, prosthetic limbs, exoskeletons, among other devices, and provide information about someone’s intention to perform a particular movement. However, the redundant action of 32 muscles in the forearm and hand means that the neuromotor system can select different combinations of muscular activities to perform the
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EISATC-Fusion: Inception Self-Attention Temporal Convolutional Network Fusion for Motor Imagery EEG Decoding IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-27 Guangjin Liang, Dianguo Cao, Jinqiang Wang, Zhongcai Zhang, Yuqiang Wu
The motor imagery brain-computer interface (MI-BCI) based on electroencephalography (EEG) is a widely used human-machine interface paradigm. However, due to the non-stationarity and individual differences among subjects in EEG signals, the decoding accuracy is limited, affecting the application of the MI-BCI. In this paper, we propose the EISATC-Fusion model for MI EEG decoding, consisting of inception
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Lower-Limb Exoskeletons Appeal to Both Clinicians and Older Adults, Especially for Fall Prevention and Joint Pain Reduction IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-27 Michael Raitor, Sandra Waugh Ruggles, Scott L. Delp, C. Karen Liu, Steven H. Collins
Exoskeletons are a burgeoning technology with many possible applications to improve human life; focusing the effort of exoskeleton research and development on the most important features is essential for facilitating adoption and maximizing positive societal impact. To identify important focus areas for exoskeleton research and development, we conducted a survey with 154 potential users (older adults)
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OPM-MEG Measuring Phase Synchronization on Source Time Series: Application in Rhythmic Median Nerve Stimulation IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-26 Yu-Yu Ma, Yang Gao, Huan-Qi Wu, Xiao-Yu Liang, Yong Li, Hao Lu, Chang-Zeng Liu, Xiao-Lin Ning
The magnetoencephalogram (MEG) based on array optically pumped magnetometers (OPMs) has the potential of replacing conventional cryogenic superconducting quantum interference device. Phase synchronization is a common method for measuring brain oscillations and functional connectivity. Verifying the feasibility and fidelity of OPM-MEG in measuring phase synchronization will help its widespread application
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TBEEG: A Two-Branch Manifold Domain Enhanced Transformer Algorithm for Learning EEG Decoding IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-25 Yanjun Qin, Wenqi Zhang, Xiaoming Tao
The electroencephalogram-based (EEG) brain-computer interface (BCI) has garnered significant attention in recent research. However, the practicality of EEG remains constrained by the lack of efficient EEG decoding technology. The challenge lies in effectively translating intricate EEG into meaningful, generalizable information. EEG signal decoding primarily relies on either time domain or frequency
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Assessment of Sensorized Insoles in Balance and Gait in Individuals With Parkinson’s Disease IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-25 Andrea Pergolini, Thomas Bowman, Tiziana Lencioni, Alberto Marzegan, Mario Meloni, Maria Chiara Carrozza, Emilio Trigili, Nicola Vitiello, Davide Cattaneo, Simona Crea
Individuals with Parkinson’s disease (PD) are characterized by gait and balance disorders limiting their independence and quality of life. Home-based rehabilitation programs, combined with drug therapy, demonstrated to be beneficial in the daily-life activities of PD subjects. Sensorized shoes can extract balance- and gait-related data in home-based scenarios and allow clinicians to monitor subjects’
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Tracking the Immediate and Short-Term Effects of Continuous Theta Burst Stimulation on Dynamic Brain States IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-25 Chao Chen, Zhidong Guo, Weiwei Peng, Shengpei Wang, Shuang Qiu, Jing Zhang, Xiaogang Chen, Huiguang He
Continuous Theta Burst Stimulation (cTBS) has been shown to modulate cortical oscillations and induce cortical inhibitory effects. Electroencephalography (EEG) studies have shown some immediate effects of cTBS on brain activity. To investigate both immediate effects and short-term effects of cTBS on dynamic brain changes, cTBS was applied to 22 healthy participants over their left motor cortex. We
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Identification of Neural and Non-Neural Origins of Joint Hyper-Resistance Based on a Novel Neuromechanical Model IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-25 Jente Willaert, Kaat Desloovere, Anja Van Campenhout, Lena H. Ting, Friedl De Groote
Joint hyper-resistance is a common symptom in neurological disorders. It has both neural and non-neural origins, but it has been challenging to distinguish different origins based on clinical tests alone. Combining instrumented tests with parameter identification based on a neuromechanical model may allow us to dissociate the different origins of joint hyper-resistance in individual patients. However
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Dynamics of Center of Pressure Trajectory in Gait: Unilateral Transfemoral Amputees Versus Non-Disabled Individuals IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-22 Yufan He, Mingyu Hu, Abu Jor, Hiroaki Hobara, Fan Gao, Toshiki Kobayashi
The primary goal of rehabilitation for individuals with lower limb amputation, particularly those with unilateral transfemoral amputation (uTFA), is to restore their ability to walk independently. Effective control of the center of pressure (COP) during gait is vital for maintaining balance and stability, yet it poses a significant challenge for individuals with uTFA. This study aims to study the COP
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Enhancing SSVEP-BCI Performance Under Fatigue State Using Dynamic Stopping Strategy IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-22 Yuheng Han, Yufeng Ke, Ruiyan Wang, Tao Wang, Dong Ming
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have emerged as a prominent technology due to their high information transfer rate, rapid calibration time, and robust signal-to-noise ratio. However, a critical challenge for practical applications is performance degradation caused by user fatigue during prolonged use. This work proposes novel methods to address this
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Novel Wearable Device for Mindful Sensorimotor Training: Integrating Motor Decoding and Somatosensory Stimulation for Neurorehabilitation IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-21 Mirka Buist, Shahrzad Damercheli, Jan Zbinden, Minh Tat Nhat Truong, Enzo Mastinu, Max Ortiz-Catalan
Sensorimotor impairment is a prevalent condition requiring effective rehabilitation strategies. This study introduces a novel wearable device for Mindful Sensorimotor Training (MiSMT) designed for sensory and motor rehabilitation. Our MiSMT device combines motor training using myoelectric pattern recognition along sensory training using two tactile displays. This device offers a comprehensive solution
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Performance of the Action Observation-Based Brain–Computer Interface in Stroke Patients and Gaze Metrics Analysis IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-21 Xin Zhang, Lin He, Qiang Gao, Ning Jiang
Brain-computer interfaces (BCIs) are anticipated to improve the efficacy of rehabilitation for people with motor disabilities. However, applying BCI in clinical practice is still a challenge due to the great diversity of patients. In the current study, a novel action observation (AO) based BCI was proposed and tested on stroke patients. Ten non-hemineglect patients and ten hemineglect patients were
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Automated Diagnosis of Major Depressive Disorder With Multi-Modal MRIs Based on Contrastive Learning: A Few-Shot Study IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-21 Tongtong Li, Yuhui Guo, Ziyang Zhao, Miao Chen, Qiang Lin, Xiping Hu, Zhijun Yao, Bin Hu
Depression ranks among the most prevalent mood-related psychiatric disorders. Existing clinical diagnostic approaches relying on scale interviews are susceptible to individual and environmental variations. In contrast, the integration of neuroimaging techniques and computer science has provided compelling evidence for the quantitative assessment of major depressive disorder (MDD). However, one of the
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MRI Compatible Lumbopelvic Movement Measurement System to Validate and Capture Task Performance During Neuroimaging IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-21 Ahyoung Song, Kerrigan Sunday, Sheri P. Silfies, Jennifer M. C. Vendemia
Research suggests that structural and functional changes within the brain are associated with chronic low back pain, and these cortical alterations might contribute to impaired sensorimotor control of the trunk and hips in this population. However, linking sensorimotor brain changes with altered movement of the trunk and hips during task-based neuroimaging presents significant challenges. An MRI-safe