사용자 맞춤형 SSVEP-BCI 철자 입력기를 위한 적응 및 선택 기반 태스크 관련 성분 분석

초록

Individual-specific steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) rely on individual data calibration, achieving high performance. However, existing methods use generalized channels and task-related subspaces, which limit their potential and lead to suboptimal solutions. To address this, we propose an adaptive selection strategy called AdapTRCA for developing a purely individual-specific SSVEP-BCI speller. Adap- TRCA optimizes both channel and subspace selection. The channel selection process constrains sparse learning by spatial distance to identify the optimal subject-specific channels, while subspace selection adaptively determines the optimal number of subject-specific task-related subspaces by maximizing profile likelihood. Extensive experiments on two publicly available datasets with 40 classes show that AdapTRCA selects more meaningful channel subsets and determines the proper number of task-related subspaces compared to traditional methods. Moreover, when inte- grated with advanced calibration-based SSVEP decoding methods, AdapTRCA fully leverages the potential of indi- vidual-specific SSVEP-BCI. In conclusion, AdapTRCA enhances the performance of user-specific SSVEP-BCI spellers, promoting their practical application.

키워드