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

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

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...