The brain-computer interface (BCI) based on rapid serial visual presentation (RSVP) provides a novel approach for efficiently optimizing traditional machine-based target detection, revealing a broad application prospect in security, entrainment, monitoring, etc. A bottleneck of current RSVP-BCI is that its detectable result is limited to a binary way, i.e., target vs. non-target, more detailed and important information about targets, such as the precise position, remains undetectable. To solve this problem, this study investigated the relationship between targets positions (up, down, left, right) and electroencephalogram (EEG) characteristics, and tested the separability of EEGs induced by the four targets positions in an online RSVP-BCI. Twelve healthy subjects participated in this study, event-related potential (ERP), topographies, laterality index (LI), discriminant canonical pattern matching (DCPM) methods were used to analyzed the EEG data. Consequently, left-right targets induced ipsilateral ERPs between bilateral hemispheres; when targets appeared at up and down positions, opposite ERPs were found between frontal and occipital areas; up-down and left-right difference reached its maximum in the 140~190ms and 190~240ms temporal window, respectively. Single-trial classification showed five-class balanced accuracy (BACC) (non-target, target at up/ down/ left/ right position) was 71.02% and 67.91% for offline and online sessions, respectively. The results provide new understanding of the RSVP features for developing BCIs.
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