Cross-Subject Transfer Method Based on Domain Generalization for Facilitating Calibration of SSVEP-Based BCIs

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 31; pp. 3307 - 3319
• Main Authors: Huang, Jiayang, Zhang, Zhi-Qiang, Xiong, Bang, Wang, Quan, Wan, Bo, Li, Fengqi, Yang, Pengfei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain-computer interfaces (BCIs); Calibration; Correlation; Correlation coefficient; Correlation coefficients; cross-subject; Data collection; domain generalization; Electroencephalography; Fatigue; Filters; Human-computer interface; Spatial filtering; Steady-state; steady-state visual evoked potential (SSVEP); Target detection; Training; Transfer learning; Visual evoked potentials; Visualization
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2023.3305202

In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), various spatial filtering methods based on individual calibration data have been proposed to alleviate the interference of spontaneous activities in SSVEP signals for enhancing the SSVEP detection performance. However, the time-consuming calibration session would increase the visual fatigue of subjects and reduce the usability of the BCI system. The key idea of this study is to propose a cross-subject transfer method based on domain generalization, which transfers the domain-invariant spatial filters and templates learned from source subjects to the target subject with no access to the EEG data from the target subject. The transferred spatial filters and templates are obtained by maximizing the intra- and inter-subject correlations using the SSVEP data corresponding to the target and its neighboring stimuli. For SSVEP detection of the target subject, four types of correlation coefficients are calculated to construct the feature vector. Experimental results estimated with three SSVEP datasets show that the proposed cross-subject transfer method improves the SSVEP detection performance compared to state-of-art methods. The satisfactory results demonstrate that the proposed method provides an effective transfer learning strategy requiring no tedious data collection process for new users, holding the potential of promoting practical applications of SSVEP-based BCI.