Motor imagery recognition with automatic EEG channel selection and deep learning
Objective. Modern motor imagery (MI)-based brain computer interface systems often entail a
large number of electroencephalogram (EEG) recording channels. However, irrelevant or …
large number of electroencephalogram (EEG) recording channels. However, irrelevant or …
Classification of motor imagery based on multi-scale feature extraction and the channeltemporal attention module
Motor imagery (MI) is a popular paradigm for controlling electroencephalogram (EEG) based
Brain-Computer Interface (BCI) systems. Many methods have been developed to attempt to …
Brain-Computer Interface (BCI) systems. Many methods have been developed to attempt to …
MIN2Net: End-to-end multi-task learning for subject-independent motor imagery EEG classification
P Autthasan, R Chaisaen… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow
control of several applications by decoding neurophysiological phenomena, which are …
control of several applications by decoding neurophysiological phenomena, which are …
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 …
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 …
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 …
Mi-bminet: An efficient convolutional neural network for motor imagery brain–machine interfaces with eeg channel selection
A brain–machine interface (BMI) based on motor imagery (MI) enables the control of devices
using brain signals while the subject imagines performing a movement. It plays a key role in …
using brain signals while the subject imagines performing a movement. It plays a key role in …
EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification
Objective. Classification of electroencephalography (EEG)-based motor imagery (MI) is a
crucial non-invasive application in brain–computer interface (BCI) research. This paper …
crucial non-invasive application in brain–computer interface (BCI) research. This paper …
A cross-space CNN with customized characteristics for motor imagery EEG classification
Y Hu, Y Liu, S Zhang, T Zhang, B Dai… - … on Neural Systems …, 2023 - ieeexplore.ieee.org
The classification of motor imagery-electroencephalogram (MI-EEG) based brain-computer
interface (BCI) can be used to decode neurological activities, which has been widely applied …
interface (BCI) can be used to decode neurological activities, which has been widely applied …
A novel multi-branch hybrid neural network for motor imagery EEG signal classification
W Ma, H Xue, X Sun, S Mao, L Wang, Y Liu… - … Signal Processing and …, 2022 - Elsevier
As a typical spontaneous brain-computer interface system, motor imagery has been widely
used in areas such as robot control and stroke rehabilitation. Recently, researchers have …
used in areas such as robot control and stroke rehabilitation. Recently, researchers have …