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Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-14 Shuhao Ma, Jie Zhang, Chaoyang Shi, Pei Di, Ian D. Robertson, Zhi-Qiang Zhang
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Improving Walking Path Generation through Biped Constraint in Indoor Navigation System for Visually Impaired Individuals IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-11 Qingquan Na, Hui Zhou, Hailei Yuan, Mengfan Gui, Hongjing Teng
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Lower-Limb Myoelectric Calibration Postures for Transtibial Prostheses IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-07 Ryan R. Posh, Emmalynn C. Barry, James P. Schmiedeler, Patrick M. Wensing
The use of an agonist-antagonist muscle pair for myoelectric control of a transtibial prosthesis requires normalizing the myoelectric signals and identifying their co-contraction signature. Extensive literature has explored the relationship between body posture and lower-limb muscle activation level using surface electromyography (EMG), but it is unknown how these relationships hold after amputation
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Brain Network Evaluation by Functional-Guided Effective Connectivity Reinforcement Learning Method Indicates Therapeutic Effect for Tinnitus IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-07 Han Lv, Jinduo Liu, Qian Chen, Junzhong Ji, Jihao Zhai, Zuozhen Zhang, Zhaodi Wang, Shusheng Gong, Zhenchang Wang
Using functional connectivity (FC) or effective connectivity (EC) alone cannot effectively delineate brain networks based on functional magnetic resonance imaging (fMRI) data, limiting the understanding of the mechanism of tinnitus and its treatment. Investigating brain FC is a foundational step in exploring EC. This study proposed a functionally guided EC (FGEC) method based on reinforcement learning
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EEG-Based Brain Functional Network Analysis for Differential Identification of Dementia-Related Disorders and Their Onset IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-07 Abdulyekeen T. Adebisi, Ho-Won Lee, Kalyana C. Veluvolu
Diagnosing and treating dementia, including mild cognitive impairment (MCI), is challenging due to diverse disease types and overlapping symptoms. Early MCI detection is vital as it can precede dementia, yet distinguishing it from later stage dementia is intricate due to subtle symptoms. The primary objective of this study is to adopt a complex network perspective to unravel the underlying pathophysiological
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Amplitude Adaptive Modulation of Neural Oscillations Over Long-Term Dynamic Conditions: A Computational Study IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-06 Zhaoyu Quan, Yan Li, Xi Cheng, Yingnan Nie, Shouyan Wang
Closed-loop deep brain stimulation (DBS) shows great potential for precise neuromodulation of various neurological disorders, particularly Parkinson’s disease (PD). However, substantial challenges remain in clinical translation due to the complex programming procedure of closed-loop DBS parameters. In this study, we proposed an online optimized amplitude adaptive strategy based on the particle swarm
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Optimizing Visual Stimulation Paradigms for User-Friendly SSVEP-Based BCIs IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-05 Meng Gu, Weihua Pei, Xiaorong Gao, Yijun Wang
In steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems, traditional flickering stimulation patterns face challenges in achieving a trade-off in both BCI performance and visual comfort across various frequency bands. To investigate the optimal stimulation paradigms with high performance and high comfort for each frequency band, this study systematically compared
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An OpenSim-Based Closed-Loop Biomechanical Wrist Model for Subject-Specific Pathological Tremor Simulation IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-05 Wellington C. Pinheiro, Henrique B. Ferraz, Maria Claudia F. Castro, Luciano L. Menegaldo
Objective: A pathological tremor (PT) is an involuntary rhythmic movement of varying frequency and amplitude that affects voluntary motion, thus compromising individuals’ independence. A comprehensive model incorporating PT’s physiological and biomechanical aspects can enhance our understanding of the disorder and provide valuable insights for therapeutic approaches. This study aims to build a biomechanical
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EMG-based Multi-User Hand Gesture Classification via Unsupervised Transfer Learning Using Unknown Calibration Gestures IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-04 Haojie Shi, Xinyu Jiang, Chenyun Dai, Wei Chen
The poor generalization performance and heavy training burden of the gesture classification model contribute as two main barriers that hinder the commercialization of sEMG-based human-machine interaction (HMI) systems. To overcome these challenges, eight unsupervised transfer learning (TL) algorithms developed on the basis of convolutional neural networks (CNNs) were explored and compared on a dataset
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User Training With Error Augmentation for sEMG-Based Gesture Classification IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-01 Yunus Bicer, Niklas Smedemark-Margulies, Basak Celik, Elifnur Sunger, Ryan Orendorff, Stephanie Naufel, Tales Imbiriba, Deniz Erdoğmuş, Eugene Tunik, Mathew Yarossi
We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wristband configuration. sEMG data were streamed into a machine-learning algorithm that classified hand gestures in real-time. After an initial model calibration, participants were presented with one of three types of feedback during a human-learning
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An Adaptive Hammerstein Model for FES-Induced Torque Prediction Based on Variable Forgetting Factor Recursive Least Squares Algorithm IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-29 Qinlian Yang, Yingqi Li, You Li, Manxu Zheng, Rong Song
Modeling the muscle response to functional electrical stimulation (FES) is an important step during model-based FES control system design. The Hammerstein structure is widely used in simulating this nonlinear biomechanical response. However, a fixed relationship cannot cope well with the time-varying property of muscles and muscle fatigue. In this paper, we proposed an adaptive Hammerstein model to
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A Combination Model of Shifting Joint Angle Changes With 3D-Deep Convolutional Neural Network to Recognize Human Activity IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-29 Endang Sri Rahayu, Eko Mulyanto Yuniarno, I. Ketut Eddy Purnama, Mauridhi Hery Purnomo
Research in the field of human activity recognition is very interesting due to its potential for various applications such as in the field of medical rehabilitation. The need to advance its development has become increasingly necessary to enable efficient detection and response to a wide range of movements. Current recognition methods rely on calculating changes in joint distance to classify activity
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Deep Learning for Enhanced Prosthetic Control: Real-Time Motor Intent Decoding for Simultaneous Control of Artificial Limbs IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-29 Jan Zbinden, Julia Molin, Max Ortiz-Catalan
The development of advanced prosthetic devices that can be seamlessly used during an individual’s daily life remains a significant challenge in the field of rehabilitation engineering. This study compares the performance of deep learning architectures to shallow networks in decoding motor intent for prosthetic control using electromyography (EMG) signals. Four neural network architectures, including
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3D Visual Discomfort Assessment With a Weakly Supervised Graph Convolution Neural Network Based on Inaccurately Labeled EEG IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-29 Na Lu, Xiaojie Zhao, Li Yao
Visual discomfort significantly limits the broader application of stereoscopic display technology. Hence, the accurate assessment of stereoscopic visual discomfort is a crucial topic in this field. Electroencephalography (EEG) data, which can reflect changes in brain activity, have received increasing attention in objective assessment research. However, inaccurately labeled data, resulting from the
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UI-MoCap: An Integrated UWB-IMU Circuit Enables 3D Positioning and Enhances IMU Data Transmission IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-26 Wenjuan Zhong, Lei Zhang, Zhongbo Sun, Mingjie Dong, Mingming Zhang
While inertial measurement unit (IMU)-based motion capture (MoCap) systems have been gaining popularity for human movement analysis, they still suffer from long-term positioning errors due to accumulated drift and inefficient data transmission via Wi-Fi or Bluetooth. To address this problem, this study introduces an integrated ultrawideband (UWB)-IMU system, named UI-MoCap, designed for simultaneous
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Synthetic IMU Datasets and Protocols Can Simplify Fall Detection Experiments and Optimize Sensor Configuration IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-26 Jie Tang, Bin He, Junkai Xu, Tian Tan, Zhipeng Wang, Yanmin Zhou, Shuo Jiang
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Brain Temporal-Spectral Functional Variability Reveals Neural Improvements of DBS Treatment for Disorders of Consciousness IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-22 Jiewei Lu, Jingchao Wu, Zhilin Shu, Xinyuan Zhang, Haitao Li, Siquan Liang, Jianda Han, Ningbo Yu
Deep brain stimulation (DBS) is establishing itself as a promising treatment for disorders of consciousness (DOC). Measuring consciousness changes is crucial in the optimization of DBS therapy for DOC patients. However, conventional measures use subjective metrics that limit the investigations of treatment-induced neural improvements. The focus of this study is to analyze the regulatory effects of
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Abnormal Static and Dynamic Local Functional Connectivity in First-Episode Schizophrenia: A Resting-State fMRI Study IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-22 Jie Zhou, Xiong Jiao, Qiang Hu, Lizhao Du, Jijun Wang, Junfeng Sun
Dynamic functional connectivity (FC) analyses have provided ample information on the disturbances of global functional brain organization in patients with schizophrenia. However, our understanding about the dynamics of local FC in never-treated first episode schizophrenia (FES) patients is still rudimentary. Dynamic Regional Phase Synchrony (DRePS), a newly developed dynamic local FC analysis method
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A Lightweight Dynamic Hand Orthosis With Sequential Joint Flexion Movement for Postoperative Rehabilitation of Flexor Tendon Repair Surgery IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-20 Chan Beom Park, Ji Sup Hwang, Hyun Sik Gong, Hyung-Soon Park
During the postoperative hand rehabilitation period, it is recommended that the repaired flexor tendons be continuously glided with sufficient tendon excursion and carefully managed protection to prevent adhesion with adjacent tissues. Thus, finger joints should be passively mobilized through a wide range of motion (ROM) with physiotherapy. During passive mobilization, sequential flexion of the metacarpophalangeal
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Decoding Multi-DoF Movements Using a CST-Based Force Generation Model With Single-DoF Training IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-20 Yang Xu, Yang Yu, Zeming Zhao, Xinjun Sheng
Recent developments in dexterous myoelectric prosthetics have established a hardware base for human-machine interfaces. Although pattern recognition techniques have seen successful deployment in gesture classification, their applications remain largely confined to certain specific discrete gestures. Addressing complex daily tasks demands an immediate need for precise simultaneous and proportional control
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Objective Neurophysiological Indices for the Assessment of Chronic Tinnitus Based on EEG Microstate Parameters IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-20 Yingying Wang, Peiying Zeng, Zhixiang Gu, Hongyu Liu, Shuqing Han, Xinran Liu, Xin Huang, Liyang Shao, Yuan Tao
Chronic tinnitus is highly prevalent but lacks precise diagnostic or effective therapeutic standards. Its onset and treatment mechanisms remain unclear, and there is a shortage of objective assessment methods. We aim to identify abnormal neural activity and reorganization in tinnitus patients and reveal potential neurophysiological markers for objectively evaluating tinnitus. By way of analyzing EEG
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Assessing Free-Living Postural Sway in Persons With Multiple Sclerosis IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-19 Brett M. Meyer, Jenna G. Cohen, Paolo DePetrillo, Melissa Ceruolo, David Jangraw, Nick Cheney, Andrew J. Solomon, Ryan S. McGinnis
Postural instability is associated with disease status and fall risk in Persons with Multiple Sclerosis (PwMS). However, assessments of postural instability, known as postural sway, leverage force platforms or wearable accelerometers, and are most often conducted in laboratory environments and are thus not broadly accessible. Remote measures of postural sway captured during daily life may provide a
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A Novel CNN-BiLSTM Ensemble Model With Attention Mechanism for Sit-to-Stand Phase Identification Using Wearable Inertial Sensors IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-19 Xin Chen, Shibo Cai, Longjie Yu, Xiaoling Li, Bingfei Fan, Mingyu Du, Tao Liu, Guanjun Bao
Sit-to-stand transition phase identification is vital in the control of a wearable exoskeleton robot for assisting patients to stand stably. In this study, we aim to propose a method for segmenting and identifying the sit-to-stand phase using two inertial sensors. First, we defined the sit-to-stand transition into five phases, namely, the initial sitting phase, the flexion momentum phase, the momentum
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A Novel Data Augmentation Approach Using Mask Encoding for Deep Learning-Based Asynchronous SSVEP-BCI IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-19 Wenlong Ding, Aiping Liu, Ling Guan, Xun Chen
Deep learning (DL)-based methods have been successfully employed as asynchronous classification algorithms in the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system. However, these methods often suffer from the limited amount of electroencephalography (EEG) data, leading to overfitting. This study proposes an effective data augmentation approach called EEG mask
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Cutting Edge Bionics in Highly Impaired Individuals: A Case of Challenges and Opportunities IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-16 Eric J. Earley, Jan Zbinden, Maria Munoz-Novoa, Fabian Just, Christiana Vasan, Axel Sjögren Holtz, Mona Emadeldin, Justyna Kolankowska, Björn Davidsson, Alexander Thesleff, Jason Millenaar, Stewe Jönsson, Christian Cipriani, Hannes Granberg, Paolo Sassu, Rickard Brånemark, Max Ortiz-Catalan
Highly impaired individuals stand to benefit greatly from cutting-edge bionic technology, however concurrent functional deficits may complicate the adaptation of such technology. Here, we present a case in which a visually impaired individual with bilateral burn injury amputation was provided with a novel transradial neuromusculoskeletal prosthesis comprising skeletal attachment via osseointegration
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Event-Related EEG Desynchronization Reveals Enhanced Motor Imagery From the Third Person Perspective by Manipulating Sense of Body Ownership With Virtual Reality for Stroke Patients IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-13 Xiaotian Xu, Xiaoya Fan, Jiaoyang Dong, Xiting Zhang, Zhe Song, Wei Li, Fang Pu
Virtual reality (VR)-based rehabilitation training holds great potential for post-stroke motor recovery. Existing VR-based motor imagery (MI) paradigms mostly focus on the first-person perspective, and the benefit of the third-person perspective (3PP) remains to be further exploited. The 3PP is advantageous for movements involving the back or those with a large range because of its field coverage.
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Channel Selection for Stereo- Electroencephalography (SEEG)-Based Invasive Brain-Computer Interfaces Using Deep Learning Methods IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-13 Xiaolong Wu, Guangye Li, Xin Gao, Benjamin Metcalfe, Dingguo Zhang
Brain-computer interfaces (BCIs) can enable direct communication with assistive devices by recording and decoding signals from the brain. To achieve high performance, many electrodes will be used, such as the recently developed invasive BCIs with channel numbers up to hundreds or even thousands. For those high-throughput BCIs, channel selection is important to reduce signal redundancy and invasiveness
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Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-13 Ruimin Peng, Zhenbang Du, Changming Zhao, Jingwei Luo, Wenzhong Liu, Xinxing Chen, Dongrui Wu
Cross-subject electroencephalogram (EEG) based seizure subtype classification is very important in precise epilepsy diagnostics. Deep learning is a promising solution, due to its ability to automatically extract latent patterns. However, it usually requires a large amount of training data, which may not always be available in clinical practice. This paper proposes Multi-Branch Mutual-Distillation (MBMD)
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Rapid-IAF: Rapid Identification of Individual Alpha Frequency in EEG Data Using Sequential Bayesian Estimation IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-12 Seitaro Iwama, Junichi Ushiba
Rapid and robust identification of the individual alpha frequency (IAF) in electroencephalogram (EEG) is an essential factor for successful brain-computer interface (BCI) use. Here we demonstrate an algorithm to determine the IAF from short-term resting-state scalp EEG data. First, we outlined the algorithm to determine IAF from short-term resting scalp EEG data and evaluated its reliability using
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IMU-Based Kinematics Estimation Accuracy Affects Gait Retraining Using Vibrotactile Cues IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-12 Nataliya Rokhmanova, Owen Pearl, Katherine J. Kuchenbecker, Eni Halilaj
Wearable sensing using inertial measurement units (IMUs) is enabling portable and customized gait retraining for knee osteoarthritis. However, the vibrotactile feedback that users receive directly depends on the accuracy of IMU-based kinematics. This study investigated how kinematic errors impact an individual’s ability to learn a therapeutic gait using vibrotactile cues. Sensor accuracy was computed
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Gait Intention Prediction Using a Lower-Limb Musculoskeletal Model and Long Short-Term Memory Neural Networks IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-12 Qingyao Bian, Marco Castellani, Duncan Shepherd, Jinming Duan, Ziyun Ding
The prediction of gait motion intention is essential for achieving intuitive control of assistive devices and diagnosing gait disorders. To reduce the cost associated with using multimodal signals and signal processing, we proposed a novel method that integrates machine learning with musculoskeletal modelling techniques for the prediction of time-series joint angles, using only kinematic signals. Additionally
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Neural Network Dynamics and Brain Oscillations Underlying Aberrant Inhibitory Control in Internet Addiction IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-09 Yi-Li Tseng, Yu-Kai Su, Wen-Jiun Chou, Makoto Miyakoshi, Ching-Shu Tsai, Chia-Jung Li, Sheng-Yu Lee, Liang-Jen Wang
Previous studies have reported a role of alterations in the brain’s inhibitory control mechanism in addiction. Mounting evidence from neuroimaging studies indicates that its key components can be evaluated with brain oscillations and connectivity during inhibitory control. In this study, we developed an internet-related stop-signal task with electroencephalography (EEG) signal recorded to investigate
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Classification of Action Potentials With High Variability Using Convolutional Neural Network for Motor Unit Tracking IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-09 Yixin Li, Yang Zheng, Guanghua Xu, Sicong Zhang, Renghao Liang, Run Ji
The reliable classification of motor unit action potentials (MUAPs) provides the possibility of tracking motor unit (MU) activities. However, the variation of MUAP profiles caused by multiple factors in realistic conditions challenges the accurate classification of MUAPs. The goal of this study was to propose an effective method based on the convolutional neural network (CNN) to classify MUAPs with
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The Effects of Immersion and Visuo-Tactile Stimulation on Motor Imagery in Stroke Patients are Related to the Sense of Ownership IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-09 Zhe Song, Xiting Zhang, Xiaotian Xu, Jiaoyang Dong, Wei Li, Yih-Kuen Jan, Fang Pu
Visual guided motor imagery (MI) is commonly used in stroke rehabilitation, eliciting event-related desynchronization (ERD) in EEG. Previous studies found that immersion level and visuo-tactile stimulation could modulate ERD during visual guided MI, and both of two factors could also improve sense of ownership (SOO) over target limb (or body). Additionally, the relationship was also reported between
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A Minimal and Multi-Source Recording Setup for Ankle Joint Kinematics Estimation During Walking Using Only Proximal Information From Lower Limb IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-09 Rami Mobarak, Andrea Tigrini, Federica Verdini, Ali H. Al-Timemy, Sandro Fioretti, Laura Burattini, Alessandro Mengarelli
In this study, a minimal setup for the ankle joint kinematics estimation is proposed relying only on proximal information of the lower-limb, i.e. thigh muscles activity and joint kinematics. To this purpose, myoelectric activity of Rectus Femoris (RF), Biceps Femoris (BF), and Vastus Medialis (VM) were recorded by surface electromyography (sEMG) from six healthy subjects during unconstrained walking
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Multi Degree of Freedom Hybrid FES and Robotic Control of the Upper Limb IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-08 Nathan Dunkelberger, Skye A. Carlson, Jeffrey Berning, Eric M. Schearer, Marcia K. O’Malley
Individuals who have suffered a spinal cord injury often require assistance to complete daily activities, and for individuals with tetraplegia, recovery of upper-limb function is among their top priorities. Hybrid functional electrical stimulation (FES) and exoskeleton systems have emerged as a potential solution to provide upper limb movement assistance. These systems leverage the user’s own muscles
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Analysis of Gaze, Head Orientation, and Joint Attention in Autism With Triadic VR Interviews IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-08 Saygin Artiran, Poorva S. Bedmutha, Pamela Cosman
Effective use of gaze and head orientation can strengthen the sense of inclusion in multi-party interactions, including job interviews. Not making significant eye contact with the interlocutors, or not turning towards them, may be interpreted as disinterest, which could worsen job interview outcomes. This study aims to support the situational solo practice of gaze behavior and head orientation using
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Modulatory Effect of Low-Intensity Transcranial Ultrasound Stimulation on Behaviour and Neural Oscillation in Mouse Models of Alzheimer’s Disease IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-08 Huifang Yang, Jiaqing Yan, Hui Ji, Mengran Wang, Teng Wang, Huiling Yi, Lanxiang Liu, Xin Li, Yi Yuan
Transcranial ultrasound stimulation (TUS) is a noninvasive brain neuromodulation technique. The application of TUS for Alzheimer’s disease (AD) therapy has not been widely studied. In this study, a long-term course (28 days) of TUS was used to stimulate the hippocampus of APP/PS1 mice. We examined the modulatory effect of TUS on behavior and neural oscillation in AD mice. We found that TUS can 1) improve
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A Kinematic Model to Predict a Continuous Range of Human-Like Walking Speed Transitions IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-08 Greggory F. Murray, Anne E. Martin
While constant speed gait is well understood, far less is known about how humans change walking speed. It is also unknown if the transition steps smoothly morph between speeds, or if they are unique. Using data from a prior study in which subjects transitioned between five speeds while walking on a treadmill, joint kinematic data were decomposed into trend and periodic components. The trend captured
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Development and Evaluation of Refreshable Braille Display and Active Touch-Reading System for Digital Reading of the Visually Impaired IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-07 Dapeng Chen, Yunjie Zhang, Xuhui Hu, Geng Chen, Yingping Fang, Xu Chen, Jia Liu, Aiguo Song
The traditional way of reading through Braille books is constraining the reading experience of blind or visually impaired (BVI) in the digital age. In order to improve the reading convenience of BVI, this paper proposes a low-cost and refreshable Braille display device, and solves the problems of high energy consumption and low latching force existing in existing devices. Further, the Braille display
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Gaze Patterns in Children With Autism Spectrum Disorder to Emotional Faces: Scanpath and Similarity IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-05 Wei Zhou, Minqiang Yang, Jingsheng Tang, Juan Wang, Bin Hu
Autism spectrum disorder (ASD) one of the fastest-growing diseases in the world is a group of neurodevelopmental disorders. Eye movement as a biomarker and clinical manifestation represents unconscious brain processes that can objectively disclose abnormal eye fixation of ASD. With the aid of eye-tracking technology, plentiful methods that identify ASD based on eye movements have been developed, but
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Electrical Input Filters of Ganglion Cells in Wild Type and Degenerating rd10 Mouse Retina as a Template for Selective Electrical Stimulation IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-31 Hamed Shabani, Eberhart Zrenner, Daniel L. Rathbun, Zohreh Hosseinzadeh
Bionic vision systems are currently limited by indiscriminate activation of all retinal ganglion cells (RGCs)– despite the dozens of known RGC types which each encode a different visual message. Here, we use spike-triggered averaging to explore how electrical responsiveness varies across RGC types toward the goal of using this variation to create type-selective electrical stimuli. A battery of visual
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DiffMDD: A Diffusion-Based Deep Learning Framework for MDD Diagnosis Using EEG IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-31 Yilin Wang, Sha Zhao, Haiteng Jiang, Shijian Li, Benyan Luo, Tao Li, Gang Pan
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
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Fabrication and Validation of Sub-Cellular Carbon Fiber Electrodes IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-31 Julianna Richie, Joseph G. Letner, Autumn Mclane-Svoboda, Yu Huan, Dorsa Haji Ghaffari, Elena Della Valle, Paras R. Patel, Hillel J. Chiel, Galit Pelled, James D. Weiland, Cynthia A. Chestek
Multielectrode arrays for interfacing with neurons are of great interest for a wide range of medical applications. However, current electrodes cause damage over time. Ultra small carbon fibers help to address issues but controlling the electrode site geometry is difficult. Here we propose a methodology to create small, pointed fiber electrodes (SPFe). We compare the SPFe to previously made blowtorched
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Temporal alpha dissimilarity of ADHD brain network in comparison with CPT and CATA IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-30 Jo-Wei Lin, Zuo-Cian Fan, Shey-Cherng Tzou, Liang-Jen Wang, Li-Wei Ko
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Rejecting Unknown Gestures Based on Surface-Electromyography Using Variational Autoencoder IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-30 Qingfeng Dai, Yongkang Wong, Mohan Kankanhalli, Xiangdong Li, Weidong Geng
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
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Subject-Independent Deep Architecture for EEG-Based Motor Imagery Classification IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-30 Shadi Sartipi, Mujdat Cetin
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
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A Novel Passive Shoulder Exoskeleton Using Link Chains and Magnetic Spring Joints IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-29 Hyun-Ho Lee, Kyung-Taek Yoon, Hyun-Ho Lim, Won-Kyu Lee, Jae-Hwan Jung, Seung-Beom Kim, Young-Man Choi
Work-related musculoskeletal disorders represent a major occupational disability issue, and 53.4% of these disorders occur in the back or shoulders. Various types of passive shoulder exoskeletons have been introduced to support the weight of the upper arm and work tools during overhead work, thereby preventing injuries and improving the work environment. The general passive shoulder exoskeleton is
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A Cross-Scale Transformer and Triple-View Attention Based Domain-Rectified Transfer Learning for EEG Classification in RSVP Tasks IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-29 Jie Luo, Weigang Cui, Song Xu, Lina Wang, Huiling Chen, Yang Li
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
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Cross-Subject Motor Imagery Decoding by Transfer Learning of Tactile ERD IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-25 Yucun Zhong, Lin Yao, Gang Pan, Yueming Wang
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
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Automatic Assessment of Upper Extremity Function and Mobile Application for Self-Administered Stroke Rehabilitation IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-25 Dong-Wook Kim, Ji Eun Park, Min-Jung Kim, Seung Hwan Byun, Chung In Jung, Ha Mok Jeong, Sang Rok Woo, Kwon Haeng Lee, Myoung Hwa Lee, Jung-Woo Jung, Dayeon Lee, Byung-Ju Ryu, Seung Nam Yang, Seung Jun Baek
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
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Multi-Task Collaborative Network: Bridge the Supervised and Self-Supervised Learning for EEG Classification in RSVP Tasks IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-24 Hongxin Li, Jingsheng Tang, Wenqi Li, Wei Dai, Yaru Liu, Zongtan Zhou
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
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Dual-3DM3AD: Mixed Transformer Based Semantic Segmentation and Triplet Pre-Processing for Early Multi-Class Alzheimer’s Diagnosis IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-23 Arfat Ahmad Khan, Rakesh Kumar Mahendran, Kumar Perumal, Muhammad Faheem
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
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Analysis of the Relation Between Balance Control Subsystems: A Structural Equation Modeling Approach IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-23 Kangjia Wu, Jingyu Zhang, Alkamat Wahib Abdullah Mohsen, Zhengbo Wang, Yu Jin, Wenan Wang, Xiaohang Peng, Dezhong Yao, Pedro A. Valdes-Sosa, Peng Ren
Balance plays a crucial role in human life and social activities. Maintaining balance is a relatively complex process that requires the participation of various balance control subsystems (BCSes). However, previous studies have primarily focused on evaluating an individual’s overall balance ability or the ability of each BCS in isolation, without considering how they influence (or interact with) each
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An Upper Limb Exoskeleton Motion Generation Algorithm Based on Separating Shoulder and Arm Motion IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-22 Jiajia Wang, Shuo Pei, Junlong Guo, Anyang Dong, Bojian Liu, Yufeng Yao
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
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Robotic Leg Prosthesis: A Survey From Dynamic Model to Adaptive Control for Gait Coordination IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-22 Xin Ma, Xiaoxu Zhang, Jian Xu
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
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Motor Imagery Classification for Asynchronous EEG-Based Brain–Computer Interfaces IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-22 Huanyu Wu, Siyang Li, Dongrui Wu
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
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Multi-Scale FC-Based Multi-Order GCN: A Novel Model for Predicting Individual Behavior From fMRI IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-22 Xuyun Wen, Qumei Cao, Bin Jing, Daoqiang Zhang
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
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Resting State EEG Variability and Implications for Interpreting Clinical Effect Sizes IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-19 Eric Liu, Cidnee Luu, Lyndia C. Wu
Resting state electroencephalography (rsEEG) is widely used to investigate intrinsic brain activity, with the potential for detecting neurophysiological abnormalities in clinical conditions from neurodegenerative disease to developmental disorders. When interpreting quantitative rsEEG changes, a key question is: how much deviation from a healthy normal brain state indicates a clinically significant
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In Vivo Transcranial Acoustoelectric Brain Imaging of Different Deep Brain Stimulation Currents IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-01-19 Yijie Zhou, Yibo Song, Shasha Qi, Xizi Song, Minpeng Xu, Feng He, Dong Ming
Deep brain stimulation (DBS) is an effective treatment for neurologic disease and its clinical effect is highly dependent on the DBS leads localization and current stimulating state. However, standard human brain imaging modalities could not provide direct feedback on DBS currents spatial distribution and dynamic changes. Acoustoelectric brain imaging (AEBI) is an emerging neuroimaging method that