Enhancing Detection of SSMVEP Induced by Action Observation Stimuli Based on Task-Related Component Analysis

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 16; p. 5269
• Main Authors: Zhang, Xin, Hou, Wensheng, Wu, Xiaoying, Chen, Lin, Jiang, Ning
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 04.08.2021; MDPI
• Subjects: Accuracy; action observation (AO); Algorithms; Brain research; brain-computer interface (BCI); Correlation analysis; Experiments; Human-computer interface; Methods; Rehabilitation; Spatial filtering; steady-state motion visual evoked potential (SSMVEP); Stroke; task-related component analysis (TRCA)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21165269

Action observation (AO)-based brain-computer interface (BCI) is an important technology in stroke rehabilitation training. It has the advantage of simultaneously inducing steady-state motion visual evoked potential (SSMVEP) and activating sensorimotor rhythm. Moreover, SSMVEP could be utilized to perform classification. However, SSMVEP is composed of complex modulation frequencies. Traditional canonical correlation analysis (CCA) suffers from poor recognition performance in identifying those modulation frequencies at short stimulus duration. To address this issue, task-related component analysis (TRCA) was utilized to deal with SSMVEP for the first time. An interesting phenomenon was found: different modulated frequencies in SSMVEP distributed in different task-related components. On this basis, a multi-component TRCA method was proposed. All the significant task-related components were utilized to construct multiple spatial filters to enhance the detection of SSMVEP. Further, a combination of TRCA and CCA was proposed to utilize both advantages. Results showed that the accuracies using the proposed methods were significant higher than that using CCA at all window lengths and significantly higher than that using ensemble-TRCA at short window lengths (≤2 s). Therefore, the proposed methods further validate the induced modulation frequencies and will speed up the application of the AO-based BCI in rehabilitation.