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.

翻译：在执行团队目标下的相互依赖个人任务时，同时个体心流（同时心流）是达成团队共享心流的先决条件。检测同时心流有助于更好地理解团队成员的状态，因此对优化多用户交互系统具有重要意义。然而，目前关于同时心流的客观特征与检测方法尚缺乏探索。基于团队合作中心流的脑机制以及前人关于基于脑电图（EEG）的个体心流检测研究，本研究旨在探索与同时心流相关的显著EEG特征，以及基于EEG信号的有效检测方法。首先，我们设计了一个双人同时心流任务，并据此构建了首个同时心流的多EEG信号数据集。随后，我们探索了可能与个体心流和同时心流相关的潜在EEG信号特征，并通过多种机器学习模型验证了这些特征在同时心流检测中的有效性。结果表明：1）脑间同步特征与同时心流相关，因其提升了模型检测不同类型同时心流的性能；2）在检测同时心流时，额叶区域的特征似乎被优先关注；3）随机森林在二分类中表现最佳，而神经网络和深度神经网络3在三分类中表现最优。