Investigating EEG-based cross-session and cross-task vigilance estimation in BCI systems

Objective. The state of vigilance is crucial for effective performance in brain-computer interface (BCI) tasks, and therefore, it is essential to investigate vigilance levels in BCI tasks. Despite this, most studies have focused on vigilance levels in driving tasks rather than on BCI tasks, and the electroencephalogram (EEG) patterns of vigilance states in different BCI tasks remain unclear. This study aimed to identify similarities and differences in EEG patterns and performances of vigilance estimation in different BCI tasks and sessions. Approach. To achieve this, we built a steady-state visual evoked potential-based BCI system and a rapid serial visual presentation-based BCI system and recruited 18 participants to carry out four BCI experimental sessions over four days. Main results. Our findings demonstrate that specific neural patterns for high and low vigilance levels are relatively stable across sessions. Differential entropy features significantly differ between different vigilance levels in all frequency bands and between BCI tasks in the delta and theta frequency bands, with the theta frequency band features playing a critical role in vigilance estimation. Additionally, prefrontal, temporal, and occipital regions are more relevant to the vigilance state in BCI tasks. Our results suggest that cross-session vigilance estimation is more accurate than cross-task estimation. Significance. Our study clarifies the underlying mechanisms of vigilance state in two BCI tasks and provides a foundation for further research in vigilance estimation in BCI applications.

Automated summary

This study investigates the vigilance levels in brain-computer interface (BCI) tasks by analyzing EEG patterns and performances. The results show that specific neural patterns for high and low vigilance levels are relatively stable across sessions. Differential entropy features significantly differ between different vigilance levels and BCI tasks, with the theta frequency band features playing a critical role in vigilance estimation. This study provides a foundation for further research in vigilance estimation in BCI applications.