AUTHOR=Liu Xiuling , Shen Yonglong , Liu Jing , Yang Jianli , Xiong Peng , Lin Feng 

TITLE=Parallel Spatial–Temporal Self-Attention CNN-Based Motor Imagery Classification for BCI

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 14 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2020.587520

DOI=10.3389/fnins.2020.587520

ISSN=1662-453X

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, we use the spatial self-attention module to capture the spatial dependencies between the channels of MI EEG signals. This module updates each channel by aggregating features over all channels with a weighted summation, thus improving the classification accuracy and eliminating the artifacts caused by manual channel selection. Furthermore, the temporal self-attention module encodes the global temporal information into features for each sampling time step, so that the high-level temporal features of the MI EEG signals can be extracted in the time domain. Quantitative analysis shows that our method outperforms state-of-the-art methods for intra-subject and inter-subject classification, demonstrating its robustness and effectiveness. In terms of qualitative analysis, we perform a visual inspection of the new spatial--temporal representation estimated from the learned architecture. Finally, the proposed method is employed to realize control of drones based on EEG signal, verifying its feasibility in real-time applications.