ERP and Pupillometry Synchronization Analysis on Rapid Serial Visual Presentation of Words, Numbers, Pictures

Hybrid brain-computer interfaces (HBCI) combining eye-tracker has attracted the attentions of researchers in target recognition. However, there are still many issues to be addressed in rapid sequence visual presentation (RSVP) tasks, such as the effect of presentation rates and target types on event-related potentials (ERP) and pupillometry, synchronization analysis of electroencephalography (EEG) and eye-tracking, and so on. In this study, the RSVP experiments with three different target types of pictures, words and numbers at the presentation rates of 100 and 200 ms were conducted. EEG data and pupillometry data were synchronously collected from 20 university students. The results of ERP analysis showed that, among three different target types at the presentation rate of 100 ms, the picture P300 component had the largest amplitude and the longest latency. From the 100 ms presentation rates to 200 ms one for the three target types, the P300 amplitudes became smaller, and the P300 latencies became shorter. The results of pupillometry analysis showed that, at the presentation rates of 100 and 200 ms, the pupil dilation of pictures had the smallest amplitude and the shortest latency. At the two presentation rates, no significant differences of pupil size and latency were found for the three target types. For the early pupil dilation within 1000 ms, the picture pupil size was significantly smaller than the other ones, and the picture pupil acceleration had the largest average amplitude and the shortest latency. These pupillometry features within 1000 ms combining with the P300 features could be taken as the effective ones for target classification. Through synchronization analysis of the EEG data and pupillometry data, the effects of target type and presentation rate on ERP and pupil dilation were different. These results could contribute to developing the fusion methods between EEG and eye-tracking, and provide valuable references for the multi-target recognition of hybrid BCI based on eye-tracking.