A novel motor imagery EEG decoding method based on feature separation

Objective. Motor imagery electroencephalography (EEG) decoding is a vital technology for the brain-computer interface (BCI) systems and has been widely studied in recent years. However, the original EEG signals usually contain a lot of class-independent information, and the existing motor imagery EEG decoding methods are easily interfered by this irrelevant information, which greatly limits the decoding accuracy of these methods. Approach. To overcome the interference of the class-independent information, a motor imagery EEG decoding method based on feature separation is proposed in this paper. Furthermore, a feature separation network based on adversarial learning (FSNAL) is designed for the feature separation of the original EEG samples. First, the class-related features and class-independent features are separated by the proposed FSNAL framework, and then motor imagery EEG decoding is performed only according to the class-related features to avoid the adverse effects of class-independent features. Main results. To validate the effectiveness of the proposed motor imagery EEG decoding method, we conduct some experiments on two public EEG datasets (the BCI competition IV 2a and 2b datasets). The experimental results comparison between our method and some state-of-the-art methods demonstrates that our motor imagery EEG decoding method outperforms all the compared methods on the two experimental datasets. Significance. Our motor imagery EEG decoding method can alleviate the interference of class-independent features, and it has great application potential for improving the performance of motor imagery BCI systems in the near future.

Automated summary

The proposed motor imagery EEG decoding method based on feature separation effectively improves the decoding accuracy by separating class-related features and class-independent features using the FSNAL network. Experimental results show that the method outperforms other state-of-the-art methods on public EEG datasets, demonstrating its potential for improving the performance of motor imagery BCI systems in the future.