Parallel Spatial-Temporal Self-Attention CNN-Based Motor Imagery Classification for BCI.

ABSTRACT: 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, dynamic nature, and low signal-to-noise ratio. Designing an end-to-end framework that fully extracts the high-level features of EEG signals remains a challenge. In this study, we present a parallel spatial-temporal self-attention-based convolutional neural network for four-class MI EEG signal classification. This study is the first to define a new spatial-temporal representation of raw EEG signals that uses the self-attention mechanism to extract distinguishable spatial-temporal features. Specifically

SUBMITTER: Liu X 

PROVIDER: S-EPMC7759669 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature