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

Copyright © 2020 Liu, Shen, Liu, Yang, Xiong and Lin..

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.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:14

Enthalten in:

Frontiers in neuroscience - 14(2020) vom: 01., Seite 587520

Sprache:

Englisch

Beteiligte Personen:

Liu, Xiuling [VerfasserIn]
Shen, Yonglong [VerfasserIn]
Liu, Jing [VerfasserIn]
Yang, Jianli [VerfasserIn]
Xiong, Peng [VerfasserIn]
Lin, Feng [VerfasserIn]

Links:

Volltext

Themen:

BCI
Deep learning
EEG
Journal Article
Motor imagery
Spatial-temporal self-attention

Anmerkungen:

Date Revised 29.12.2020

published: Electronic-eCollection

Citation Status PubMed-not-MEDLINE

doi:

10.3389/fnins.2020.587520

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM31926761X