deep learning

Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation
Author(s)3: Guangyi Zhang, Ali Etemad
Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Driver vigilance estimation is an important task for transportation safety. Wearable and portable brain-computer interface devices provide a powerful means for real-time monitoring of the vigilance level of drivers to…

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Deep-Learning-Based Emergency Stop Prediction for Robotic Lower-Limb Rehabilitation Training Systems
Author(s)3: Baekdong Cha, Kyung-hwan Lee, Jeha Ryu
Deep-Learning-Based Emergency Stop Prediction for Robotic Lower-Limb Rehabilitation Training Systems 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Robotic lower-limb rehabilitation training is a better alternative for the physical training efforts of a therapist due to advantages, such as intensive repetitive motions, economical therapy, and quantitative assessment of…

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Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring
Author(s)3: Aleš Procházka, Ondřej Dostál, Pavel Cejnar, Hagar Ibrahim Mohamed, Zbyšek Pavelek, Martin Vališ, Oldřich Vyšata
Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring 526 336 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Ataxic gait monitoring and assessment of neurological disorders belong to important multidisciplinary areas that are supported by digital signal processing methods and machine learning tools. This paper presents the possibility…

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Improved High-density Myoelectric Pattern Recognition Control Against Electrode Shift Using Data Augmentation and Dilated Convolutional Neural NetworkT
Author(s)3: Le Wu, Xu Zhang, Kun Wang, Xiang Chen, Xun Chen
Improved High-density Myoelectric Pattern Recognition Control Against Electrode Shift Using Data Augmentation and Dilated Convolutional Neural NetworkT 553 217 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
The objective of this work is to develop a robust method for myoelectric control towards alleviating the in-terference of electrode shift. Methods: In the proposed method, a preprocessing approach was first performed to convert high-den-sity surface electromyogram (HD-sEMG) signals into a series of images, and the electrode shift appeared as pixel shift in these im-ages. read more

Deep Learning Architecture to Assist with Steering a Powered Wheelchair
Author(s)3: Malik J. Haddad, David A. Sanders
Deep Learning Architecture to Assist with Steering a Powered Wheelchair 582 322 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
This paper describes a novel Deep Learning architecture to assist with steering a powered wheelchair. A rule-based approach is utilized to train and test a Long Short Term Memory (LSTM) Neural Network. It is the first time a LSTM has been used for steering a powered wheelchair. read more

Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach
Author(s)3: Guangyu Jia, Hak-Keung Lam, Shichao Ma, Zhaohui Yang, Yujia Xu, Bo Xiao
Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach 874 501 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Understanding and classifying electromyogram (EMG) signals is of significance for dexterous prosthetic hand control, sign languages, grasp recognition, human-machine interaction, etc.. The existing research of EMG-based hand gesture classification faces…

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Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
Author(s)3: Ulysse Côté-Allard, Cheikh Latyr Fall, Alexandre Drouin, Alexandre Campeau-Lecours, Clément Gosselin, Kyrre Glette, François Laviolette, Benoit Gosselin
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning 780 435 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

        In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field…

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Learning Spatial–Spectral–Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment

Learning Spatial–Spectral–Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment
Author(s)3: Pengbo Zhang, Xue Wang, Weihang Zhang, Junfeng Chen
Learning Spatial–Spectral–Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment 780 395 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

    Mental workload assessment is essential for maintaining human health and preventing accidents. Most research on this issue is limited to a single task. However, cross-task assessment is indispensable for…

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A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series
Author(s)3: Stanislas Chambon, Mathieu N. Galtier, Pierrick J. Arnal, Gilles Wainrib, Alexandre Gramfort
A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series 780 364 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

     Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of…

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Mixed Neural Network Approach for Temporal Sleep Stage Classification
Author(s)3: Hao Dong, Akara Supratak, Wei Pan, Chao Wu, Paul M. Matthews, Yike Guo
Mixed Neural Network Approach for Temporal Sleep Stage Classification 780 1165 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

      This paper proposes a practical approach to addressing limitations posed by using of single-channel electroencephalography (EEG) for sleep stage classification. EEG-based characterizations of sleep stage progression contribute the diagnosis…

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TNSRE serves the community of biomedical engineers and clinical researchers who work at the intersection of neuroscience and physical medicine. We publish novel approaches and technologies for better understanding neural systems, human movement, and the relationships between them, with a focus on assistive devices that improve life for patients, for practicing clinicians, and for everyday use.

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