A comfortable steady state visual evoked potential stimulation paradigm using peripheral vision. (20th April 2021)

• Record Type: Journal Article
• Authors: Zhao, Xi; Wang, Zhenyu; Zhang, Min; Hu, Honglin
• Is Part Of: Journal of neural engineering. Volume 18:Number 5(2021)
• Publication Date: 2021-04-20
• Subjects: electroencephalography (EEG) -- steady-state visual evoked potential (SSVEP) -- comfortable stimulation paradigm -- task-related component analysis (TRCA) -- brain–computer interface (BCI); Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/abf397
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . Steady-state visual evoked potential (SSVEP)-brain–computer interfaces (BCIs) can cause much visual discomfort if the users use the SSVEP-BCIs for a long time. As an alternative scheme to reduce users' visual fatigue, this study proposes a new stimulation paradigm (termed as steady state peripheral visual evoked potential, abbreviated as SSPVEP) which makes full use of peripheral vision. The electroencephalography (EEG) signals are classifiable which means this proposed stimulation paradigm can be used in BCI system with the aid of the latest hybrid signal processing approach. Approach . Under the SSPVEP stimulation paradigm, 20 targets are mounted on 20 frequencies and other targets are set between two targets with flicker stimuli coding. In order to ensure the classification accuracy of SSPVEP signal detection under the proposed stimulation paradigm, two optimization schemes are proposed for the detection stage of the conventional ensemble task-related component analysis (ETRCA) algorithm. The first optimization scheme uses nonlinear correlation coefficient at the detection part for the first time to improve the classification accuracy of the system. The second optimization scheme uses γ correction to enhance the time domain features of the SSPVEP signals, and uses Manhattan distance for the final detection. Main results . According to the response waveforms of the EEG signals generated under the SSPVEP stimulation paradigm and the results of the questionnaire on user's comfort level to the two stimulation paradigms (SSPVEP paradigm and conventional SSVEP paradigm), the proposed stimulation paradigm brings less visual fatigue. The comparison results indicate that the proposed detection methods (ETRCA + γ correction + Manhattan distance, ETRCA + Spearman correlation) can greatly improve the classification accuracy compared with the individual template canonical correlation analysis method and conventional ETRCA method based on Pearson correlation. Significance . The SSPVEP stimulation paradigm reduces users' visual fatigue via using peripheral vision, which provides a new design idea for SSVEP stimulation paradigm aimed at visual comfort.