Parallel spatial-temporal self-attention CNN-based motor imagery classification for BCI

Parallel spatial-temporal self-attention CNN-based motor imagery classification for BCI

Motor imagery (MI) electroencephalography (EEG) classification is an important part of the brain-computer interface (BCI), allowing people with mobility problems to communicate with the outside world via assistive devices. However, EEG decoding is a challenging task because of its complexity, dynami...

Full description

Bibliographic Details
Main Authors: Liu, Xiuling, Shen, Yonglong, Liu, Jing, Yang, Jianli, Xiong, Peng, Lin, Feng
Other Authors: School of Computer Science and Engineering
Format: Journal Article
Language:English
Published: 2021
Subjects:
Engineering::Computer science and engineering
Motor Imagery
EEG
Online Access:https://hdl.handle.net/10356/146014

Internet

https://hdl.handle.net/10356/146014

Similar Items