Graph convolution neural network based end-to-end channel selection and classification for motor imagery brain–computer interfaces
B Sun, Z Liu, Z Wu, C Mu, T Li - IEEE transactions on industrial …, 2022 - ieeexplore.ieee.org
Classification of electroencephalogram-based motor imagery (MI-EEG) tasks is crucial in
brain–computer interface (BCI). EEG signals require a large number of channels in the …
brain–computer interface (BCI). EEG signals require a large number of channels in the …
[HTML][HTML] A learnable EEG channel selection method for MI-BCI using efficient channel attention
L Tong, Y Qian, L Peng, C Wang, ZG Hou - Frontiers in Neuroscience, 2023 - frontiersin.org
Introduction During electroencephalography (EEG)-based motor imagery-brain-computer
interfaces (MI-BCIs) task, a large number of electrodes are commonly used, and consume …
interfaces (MI-BCIs) task, a large number of electrodes are commonly used, and consume …
[HTML][HTML] A parallel multiscale filter bank convolutional neural networks for motor imagery EEG classification
Objective Electroencephalogram (EEG) based brain–computer interfaces (BCI) in motor
imagery (MI) have developed rapidly in recent years. A reliable feature extraction method is …
imagery (MI) have developed rapidly in recent years. A reliable feature extraction method is …
A multi-view CNN with novel variance layer for motor imagery brain computer interface
Accurate and robust classification of Motor Imagery (MI) from Electroencephalography (EEG)
signals is among the most challenging tasks in Brain-Computer Interface (BCI) field. To …
signals is among the most challenging tasks in Brain-Computer Interface (BCI) field. To …
[HTML][HTML] Enhancing cross-subject motor imagery classification in EEG-based brain–computer interfaces by using multi-branch CNN
A brain–computer interface (BCI) is a computer-based system that allows for communication
between the brain and the outer world, enabling users to interact with computers using …
between the brain and the outer world, enabling users to interact with computers using …
[HTML][HTML] TCACNet: Temporal and channel attention convolutional network for motor imagery classification of EEG-based BCI
Brain–computer interface (BCI) is a promising intelligent healthcare technology to improve
human living quality across the lifespan, which enables assistance of movement and …
human living quality across the lifespan, which enables assistance of movement and …
LSTM-based EEG classification in motor imagery tasks
P Wang, A Jiang, X Liu, J Shang… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
Classification of motor imagery electroencephalograph signals is a fundamental problem in
brain–computer interface (BCI) systems. We propose in this paper a classification framework …
brain–computer interface (BCI) systems. We propose in this paper a classification framework …
An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces
AM Roy - Biomedical Signal Processing and Control, 2022 - Elsevier
Objective Electroencephalogram (EEG) based motor imagery (MI) classification is an
important aspect in brain-machine interfaces (BMIs) which bridges between neural system …
important aspect in brain-machine interfaces (BMIs) which bridges between neural system …
[HTML][HTML] Motor imagery EEG classification using capsule networks
KW Ha, JW Jeong - Sensors, 2019 - mdpi.com
Various convolutional neural network (CNN)-based approaches have been recently
proposed to improve the performance of motor imagery based-brain-computer interfaces …
proposed to improve the performance of motor imagery based-brain-computer interfaces …
[HTML][HTML] EEG classification of motor imagery using a novel deep learning framework
M Dai, D Zheng, R Na, S Wang, S Zhang - Sensors, 2019 - mdpi.com
Successful applications of brain-computer interface (BCI) approaches to motor imagery (MI)
are still limited. In this paper, we propose a classification framework for MI …
are still limited. In this paper, we propose a classification framework for MI …