Enhancing SSVEP-Based BCI Performance via Consensus Information Transfer Among Subjects

• Published in: IEEE transaction on neural networks and learning systems Vol. 36; no. 8; pp. 13833 - 13847
• Main Authors: Zhang, Xinyi, Wei, Wei, Qiu, Shuang, Li, Xujin, Wang, Yijun, He, Huiguang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2025
• Subjects: Accuracy; Adult; Algorithms; Brain modeling; Brain-computer interface (BCI); Brain-Computer Interfaces; Consensus; Correlation; Data models; Decoding; deep learning (DL); electroencephalogram (EEG); Electroencephalography; Electroencephalography - methods; Evoked Potentials, Visual - physiology; Feature extraction; Female; Humans; Knowledge engineering; Male; Neural Networks, Computer; Photic Stimulation - methods; steady-state visual evoked potential (SSVEP); Training; Visualization; Young Adult
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2024.3506998

The brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has received considerable attention for its high communication speed. While large datasets provide an important opportunity to enhance decoding accuracies, the key challenge lies in the exploration of existing data to extract valuable information based on the distinctive characteristics of brain responses. In this study, we introduce ConsenNet, a framework designed to enhance SSVEP classification performance by leveraging information from the diverse perspectives of existing subjects. First, this study exploits the diversity of existing subjects to generate new samples, which retain both task-related components and variability. This effectively enhances the network generalization capability on new subjects. Second, the structured knowledge that encapsulates the interrelationships between categories has been constructed and then transferred from the teacher network to the student network, guiding the student network to extract invariant features across subjects. Finally, our model incorporates a small amount of new subject data for model calibration in the final stage. Offline experiments conducted on three public datasets demonstrate the superiority of ConsenNet over 19 methods compared in this study, while online experiments validate its feasibility for real-world applications.