Combining EEG signals from the 2 members of a team to improve event identification

We examined the potential of combining EEG signals from multiple individuals to identify critical events in a team task. In this study two subjects played a video game in which they had complementary roles, one player serving as a Bait to distract 5 enemy fortress and the other serving as a Shooter to destroy the fortress. Twenty-one pairs of subjects were analyzed. Critical events, destruction of the fortress and deaths of each player, evoked distinguishable P300-like responses from both players. Fortress kills could be best identified by combining the two EEG signals, while deaths could be best identified by focusing on the response of the player who died. Hidden semi-Markov models (HSMMs) achieved good identification of the events by combining information about the temporal distribution of these critical events with the conditional probability of the EEG activity. These findings indicate that we can track and improve by adaptively merging or selecting the signals from different team members. • Combining EEG signals improves detection of critical events in a team-based task. • Hidden semi-Markov models enhance event localization. • 85% of critical events are identified to within 50 msec. • Different critical events produce distinguishable P300 responses.