Changes of EEG phase synchronization and EOG signals along the use of steady state visually evoked potential-based brain computer interface. (10th July 2020)

• Record Type: Journal Article
• Authors: Peng, Yufan; Wang, Ze; Wong, Chi Man; Nan, Wenya; Rosa, Agostinho; Xu, Peng; Wan, Feng; Hu, Yong
• Is Part Of: Journal of neural engineering. Volume 17:Number 4(2020:Aug.)
• Publication Date: 2020-07-10
• Subjects: brain-computer interface -- SSVEP -- EOG -- EEG -- functional brain network -- phase synchronization; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ab933e
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . The steady-state visual evoked potential (SSVEP)-based brain computer interface (BCI) has demonstrated relatively high performance with little user training, and thus becomes a popular BCI paradigm. However, due to the performance deterioration over time, its robustness and reliability appear not sufficient to allow a non-expert to use outside laboratory. It would be thus helpful to study what happens behind the decreasing tendency of the BCI performance. Approach. This paper explores the changes of brain networks and electrooculography (EOG) signals to investigate the cognitive capability changes along the use of the SSVEP-based BCI. The EOG signals are characterized by the blink amplitudes and the speeds of saccades, and the brain networks are estimated by the instantaneous phase synchronizations of electroencephalography signals. Main results. Experimental results revealed that the characteristics derived from EOG and brain networks have similar trends which contain two stages. At the beginning, the blink amplitudes and the saccade speeds start to reduce. Meanwhile, the global synchronizations of the brain networks are formed quickly. These observations implies that the cognitive decline along the use of the SSVEP-based BCI. Then, the EOG and the brain networks related characteristics demonstrate a slow recovery or relatively stable trend. Significance. This study could be helpful for a better understanding about the depreciation of the BCI performance as well as its relationship with the brain networks and the EOG along the use of the SSVEP-based BCI.