Idle-State Detection in Multi-user Motor Imagery Brain Computer Interface with Cross-Brain CSP and Hyper-Brain-Network

Motor imagery (MI) is a kind of spontaneous controlled brain computer interface (BCI) paradigm, which is more likely to the concept of 'mind control'. The idle state detection is an important problem to construct a robust MI-BCI system since it needs to tell whether the subject is in MI task and the idle state contains much diverse cases. Herein, EEG-based multi-user BCI refers to two or more subjects engage in a coordinate task while their EEG are simultaneously recorded. The objective of this paper is to explore how the multi-user MI-BCI performance in idle detection based on CSP (common spatial pattern) and brain-network features. We proposed several strategies for cross-brain feature fusion. Results show that 1) Through CSP features, the classification accuracy of cross-brain outperforms the single brain CSP feature across different strategies. 2) Through brain-network features, the classification accuracy of concatenated with the paired subjects outperforms the single brain-network, while the inter-brain-network is lower than single subject 3) alpha frequency band shows better performance than other bands. Multi-user MI-BCI would be a potential way to improve the idle state detection accuracy.