  When executing interdependent personal tasks for the team's purpose,
simultaneous individual flow(simultaneous flow) is the antecedent condition of
achieving shared team flow. Detecting simultaneous flow helps better
understanding the status of team members, which is thus important for
optimizing multi-user interaction systems. However, there is currently a lack
exploration on objective features and methods for detecting simultaneous flow.
Based on brain mechanism of flow in teamwork and previous studies on
electroencephalogram (EEG)-based individual flow detection, this study aims to
explore the significant EEG features related to simultaneous flow, as well as
effective detection methods based on EEG signals. First, a two-player
simultaneous flow task is designed, based on which we construct the first
multi-EEG signals dataset of simultaneous flow. Then, we explore the potential
EEG signal features that may be related to individual and simultaneous flow and
validate their effectiveness in simultaneous flow detection with various
machine learning models. The results show that 1) the inter-brain synchrony
features are relevant to simultaneous flow due to enhancing the models'
performance in detecting different types of simultaneous flow; 2) the features
from the frontal lobe area seem to be given priority attention when detecting
simultaneous flows; 3) Random Forests performed best in binary classification
while Neural Network and Deep Neural Network3 performed best in ternary
classification.
