A parallel multi-scale time-frequency block convolutional neural network based on channel attention module for motor imagery classification

EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification

(2021) Ce Zhang et al.   Journal of Neural Engineering

Rehabilitation of motor function in children with cerebral palsy based on motor imagery

(2021) Jiaxin Xie et al.   Cognitive Neurodynamics

Electroencephalography-based motor imagery classification using temporal convolutional network fusion

(2021) Yazeed K. Musallam et al.   Biomedical Signal Processing and Control

Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network

(2020) Xianlun Tang et al.   EXPERT SYSTEMS WITH APPLICATIONS

HS-CNN: A CNN with hybrid convolution scale for EEG motor imagery classification

(2019) Guanghai Dai et al.   Journal of Neural Engineering

A Parallel Multiscale Filter Bank Convolutional Neural Networks for Motor Imagery EEG Classification

(2019) Hao Wu et al.   Frontiers in Neuroscience

Squeeze-and-Excitation Networks

(2019) Jie Hu et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

EEGNet: a compact convolutional neural network for EEG-based brain--computer interfaces

(2018) Vernon Lawhern et al.   Journal of Neural Engineering

Temporally Constrained Sparse Group Spatial Patterns for Motor Imagery BCI

(2018) Yu Zhang et al.   IEEE Transactions on Cybernetics

Deep learning with convolutional neural networks for EEG decoding and visualization

(2017) Robin Tibor Schirrmeister et al.   HUMAN BRAIN MAPPING

Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap

(2017) Ming-ai Li et al.   Applied Sciences-Basel

Subject-based feature extraction by using fisher WPD-CSP in brain–computer interfaces

(2016) Banghua Yang et al.   COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

Brain-computer interfaces for communication and control

(2011) Dennis J. McFarland et al.   COMMUNICATIONS OF THE ACM