Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 4; p. 1315
• Main Authors: Lee, Hyeon Kyu, Choi, Young-Seok
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 12.02.2021; MDPI AG
• Subjects: Algorithms; brain-computer interface (BCI); Brain-Computer Interfaces; canonical correlation analysis (CCA); Electroencephalography; electroencephalography (EEG); Evoked Potentials, Visual; Humans; Photic Stimulation; Physical Phenomena; steady-state visual evoked potential (SSVEP); task-related component analysis (TRCA); two-step task-related component analysis (TSTRCA); electroencephalography (EEG); canonical correlation analysis (CCA); two-step task-related component analysis (TSTRCA); brain-computer interface (BCI); task-related component analysis (TRCA); steady-state visual evoked potential (SSVEP)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21041315

Among various methods for frequency recognition of the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) study, a task-related component analysis (TRCA), which extracts discriminative spatial filters for classifying electroencephalogram (EEG) signals, has gathered much interest. The TRCA-based SSVEP method yields lower computational cost and higher classification performance compared to existing SSVEP methods. In spite of its utility, the TRCA-based SSVEP method still suffers from the degradation of the frequency recognition rate in cases where EEG signals with a short length window are used. To address this issue, here, we propose an improved strategy for decoding SSVEPs, which is insensitive to a window length by carrying out two-step TRCA. The proposed method reuses the spatial filters corresponding to target frequencies generated by the TRCA. Followingly, the proposed method accentuates features for target frequencies by correlating individual template and test data. For the evaluation of the performance of the proposed method, we used a benchmark dataset with 35 subjects and confirmed significantly improved performance comparing with other existing SSVEP methods. These results imply the suitability as an efficient frequency recognition strategy for SSVEP-based BCI applications.