A combined brain-computer interface based on P300 potentials and motion-onset visual evoked potentials

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• 作者：Jing Jin^a ; ^{Jinjingat@gmail.com} ; Brendan Z. Allison^b ; Xingyu Wang^a ; Christa Neuper^a ; ^c
• 关键词：Event-related potential ; P300 potential ; Motion-onset visual evoked potentials ; Brain&ndash ; computer interface ; Changing stimuli ; Adaptive
• 刊名：Journal of Neuroscience Methods
• 出版年：2012
• 期刊代码：33_01650270
• 类别：neu
• 出版时间：15 April, 2012
• 卷：205
• 期：2
• 页码：265-276
• 文件大小：1935 K

摘要

Brain-computer interfaces (BCIs) allow users to communicate via brain activity alone. Many BCIs rely on the P300 and other event-related potentials (ERPs) that are elicited when target stimuli flash. Although there have been considerable research exploring ways to improve P300 BCIs, surprisingly little work has focused on new ways to change visual stimuli to elicit more recognizable ERPs. In this paper, we introduce a 鈥渃ombined鈥?BCI based on P300 potentials and motion-onset visual evoked potentials (M-VEPs) and compare it with BCIs based on each simple approach (P300 and M-VEP). Offline data suggested that performance would be best in the combined paradigm. Online tests with adaptive BCIs confirmed that our combined approach is practical in an online BCI, and yielded better performance than the other two approaches (P < 0.05) without annoying or overburdening the subject. The highest mean classification accuracy (96%) and practical bit rate (26.7 bit/s) were obtained from the combined condition.