期刊
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
卷 31, 期 -, 页码 991-1000出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2023.3237319
关键词
Brain-computer interfaces (BCIs); capsule network; P300; attention
A brain-computer interface (BCI) has great potential in various applications, and the P300-based BCI speller is a typical example. However, the low recognition rate of the P300 speller is a challenge due to the complex spatio-temporal characteristics of EEG signals. In this study, we proposed a deep-learning framework named ST-CapsNet to improve P300 detection using a capsule network with spatial and temporal attention modules.
A brain-computer interface (BCI), which provides an advanced direct human-machine interaction, has gained substantial research interest in the last decade for its great potential in various applications including rehabilitation and communication. Among them, the P300-based BCI speller is a typical application that is capable of identifying the expected stimulated characters. However, the applicability of the P300 speller is hampered for the low recognition rate partially attributed to the complex spatio-temporal characteristics of the EEG signals. Here, we developed a deep-learning analysis framework named ST-CapsNet to overcome the challenges regarding better P300 detection using a capsule network with both spatial and temporal attention modules. Specifically, we first employed spatial and temporal attention modules to obtain refined EEG signals by capturing event-related information. Then the obtained signals were fed into the capsule network for discriminative feature extraction and P300 det- ection. In order to quantitatively assess the performance of the proposed ST-CapsNet, two publicly-available datasets (i.e., Dataset IIb of BCI Competition 2003 and Dataset II of BCI Competition III) were applied. A new metric of averaged symbols under repetitions (ASUR) was adopted to evaluate the cumulative effect of symbol recognition under different repetitions. In comparison with several widely-used methods (i.e., LDA, ERP-CapsNet, CNN, MCNN, SWFP, and MsCNN-TL-ESVM), the proposed ST-CapsNet framework significantly outperformed the state-of-the-art methods in terms of ASUR. More interestingly, the absolute values of the spatial filters learned by ST-CapsNet are higher in the parietal lobe and occipital region, which is consistent with the generation mechanism of P300.
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