The proposed ST-CapsNet framework significantly outperformed state-of-the-art methods in terms of averaged symbols under repetitions for P300 detection.
ST-CapsNet improves P300 detection in brain-computer interfaces by combining spatial and temporal attention with a capsule network.
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.
Wang et al. (Sun,) conducted a other in P300 detection in brain-computer interfaces. ST-CapsNet (capsule network with spatial and temporal attention modules) vs. LDA, ERP-CapsNet, CNN, MCNN, SWFP, and MsCNN-TL-ESVM was evaluated on Averaged symbols under repetitions (ASUR). The proposed ST-CapsNet framework significantly outperformed state-of-the-art methods in terms of averaged symbols under repetitions for P300 detection.