Similarity-constrained task-related component analysis for enhancing SSVEP detection

Abstract

Objective. Task-related component analysis (TRCA) is a representative subject-specific training algorithm in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces. Task-related components (TRCs), extracted by the TRCA-based spatial filtering from electroencephalogram (EEG) signals through maximizing the reproducibility across trials, may contain some task-related inherent noise that is still trial-reproducible. Approach. To address this problem, this study proposed a similarity-constrained TRCA (scTRCA) algorithm to remove the task-related noise and extract TRCs maximally correlated with SSVEPs for enhancing SSVEP detection. Similarity constraints, which were created by introducing covariance matrices between EEG training data and an artificial SSVEP template, were added to the objective function of TRCA. Therefore, a better spatial filter was obtained by maximizing not only the reproducibility across trials but also the similarity between TRCs and SSVEPs. The proposed scTRCA was compared with TRCA, multi-stimulus TRCA, and sine–cosine reference signal based on two public datasets. Main results. The performance of TRCA in target identification of SSVEPs is improved by introducing similarity constraints. The proposed scTRCA significantly outperformed the other three methods, and the improvement was more significant especially with insufficient training data. Significance. The proposed scTRCA algorithm is promising for enhancing SSVEP detection considering its better performance and robustness against insufficient calibration.