Multimodal collaborative BCI system based on the improved CSP feature extraction algorithm

• Published in: Virtual Reality & Intelligent Hardware Vol. 4; no. 1; pp. 22 - 37
• Main Authors: Cunbo Li, Ning Li, Yuan Qiu, Yueheng Peng, Yifeng Wang, Lili Deng, Teng Ma, Fali Li, Dezhong Yao, Peng Xu
• Format: Journal Article
• Language: English
• Published: KeAi Communications Co., Ltd 01.02.2022
• Subjects: Collaborative brain-computer interface (BCI); Game controlling system; Motion visual evoked potentials (mVEP); Steady-state visual evoked potential (SSVEP)
• ISSN: 2096-5796
• DOI: 10.1016/j.vrih.2022.01.002

Background: As a novel approach for people to directly communicate with an external device, the study of brain-computer interfaces (BCIs) has become well-rounded. However, similar to the real-world scenario, where individuals are expected to work in groups, the BCI systems should be able to replicate group attributes. Methods: We proposed a 4-order cumulants feature extraction method (CUM4-CSP) based on the common spatial patterns (CSP) algorithm. Simulation experiments conducted using motion visual evoked potentials (mVEP) EEG data verified the robustness of the proposed algorithm. In addition, to freely choose paradigms, we adopted the mVEP and steady-state visual evoked potential (SSVEP) paradigms and designed a multimodal collaborative BCI system based on the proposed CUM4-CSP algorithm. The feasibility of the proposed multimodal collaborative system framework was demonstrated using a multiplayer game controlling system that simultaneously facilitates the coordination and competitive control of two users on external devices. To verify the robustness of the proposed scheme, we recruited 30 subjects to conduct online game control experiments, and the results were statistically analyzed. Results: The simulation results prove that the proposed CUM4-CSP algorithm has good noise immunity. The online experimental results indicate that the subjects could reliably perform the game confrontation operation with the selected BCI paradigm. Conclusions: The proposed CUM4-CSP algorithm can effectively extract features from EEG data in a noisy environment. Additionally, the proposed scheme may provide a new solution for EEG-based group BCI research.