In real life settings, human operators work in cooperation to optimize both safety and performance. The goal of this study is to assess teammates' cooperation level using cerebral measures and machine learning techniques. We designed an experimental protocol with a modified version of the NASA MATB-Ⅱ that was performed in 8 five-minute blocks. Each participant was either Pilot Flying (PF) or Pilot Monitoring (PM) with specific sub-tasks to attend to. In half the blocks they were instructed to cooperate by helping the other with one of his/her sub-tasks. Five teams of two healthy volunteers were recruited among the students of the ISAE-SUPAERO engineering school. In addition to behavioral data, their electroencephalogram (EEG) was recorded. The cooperation level of the participants was estimated using a brain-computer interface pipeline with a classification step applied on basic connectivity features, i.e. covariance matrices computed between participants' EEG sensors. Behavioral results revealed a significant impact of cooperative instructions. Also, the implemented estimation pipeline allowed to estimate cooperative states using covariance matrices with an average accuracy of 66.6% using the signal filtered in the theta band, 64.5% for the alpha band and 65.3% for the low beta band. These preliminary estimation results are above the adjusted chance level and pave the way towards adaptive training tools based on hyperscanning for aeronautical settings.
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