In Journal of neural engineering ; h5-index 52.0
Objective.Motor Imagery Brain-Computer Interface (MI-BCI) is an active Brain-Computer Interface (BCI) paradigm focusing on the identification of motor intention, which is one of the most important non-invasive BCI paradigms. In MI-BCI studies, deep learning-based methods (especially lightweight networks) have attracted more attention in recent years, but the decoding performance still needs further improving.Approach.To solve this problem, we designed a filter bank structure with sinc-convolutional layers for spatio-temporal feature extraction of MI-EEG in four motor rhythms. The Channel Self-Attention method was introduced for feature selection based on both global and local information, so as to build a model called Filter Bank Sinc-convolutional Network with Channel Self-Attention (FB-Sinc-SCANet) for high performance MI-decoding. Also, we proposed a data augmentation method based on Multivariate Empirical Mode Decomposition (MEMD) to improve the generalization capability of the model.Main results.We performed an intra-subject evaluation experiment on the unseen data of three open MI datasets. The proposed method achieved mean accuracy of 78.20% (4-class scenario) on BCI Competition IV IIa, 87.34% (2-class scenario) on BCI Competition IV IIb, and 72.03% (2-class scenario) on OpenBMI dataset, which are significantly higher than those of compared deep learning-based methods by at least 3.05% (p=0.0469), 3.18% (p=0.0371), and 2.27% (p=0.0024) respectively.Significance.This work provides a new option for deep learning-based MI decoding, which can be employed for building BCI systems for motor rehabilitation.
Chen Jiaming, Wang Dan, Yi Weibo, Xu Meng, Tan Xiyue
2023-Feb-10
Brain–computer interface, Data Augmentation, Deep Learning, Motor Imagery, Self-Attention
