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

• المؤلفون الرئيسيون: Liu, Xiuling, Shen, Yonglong, Liu, Jing, Yang, Jianli, Xiong, Peng, Lin, Feng
• مؤلفون آخرون: School of Computer Science and Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2021
• الموضوعات: Engineering::Computer science and engineering; Motor Imagery; EEG
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/146014
• المؤسسة: 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...