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|Title:||Parallel spatial-temporal self-attention CNN-based motor imagery classification for BCI||Authors:||Liu, Xiuling
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2020||Source:||Liu, X., Shen, Y., Liu, J., Yang, J., Xiong, P., & Lin, F. (2020). Parallel Spatial–Temporal Self-Attention CNN-Based Motor Imagery Classification for BCI. Frontiers in Neuroscience, 14, 587520-. doi:10.3389/fnins.2020.587520||Journal:||Frontiers in neuroscience||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.||URI:||https://hdl.handle.net/10356/146014||ISSN:||1662-4548||DOI:||10.3389/fnins.2020.587520||Rights:||© 2020 Liu, Shen, Liu, Yang, Xiong and Lin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Journal Articles|
Updated on Jul 27, 2021
Updated on Jul 27, 2021
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