Are We in The Zone? Exploring The Features and Method of Detecting Simultaneous Flow Experiences Based on EEG Signals

TL;DR

This work tackles the problem of detecting simultaneous flow in a two-person collaborative setting using EEG. It introduces the first multi-channel EEG dataset for simultaneous flow gathered from a two-player Whack-A-Mole task, along with a feature set that includes inter-brain synchrony measures. Through extensive machine learning experiments, the authors show that inter-brain synchrony features, particularly from frontal regions, enhance both binary and ternary flow classification, with Random Forest performing best in binary tasks and NN/DNN3 excelling in ternary tasks ( accuracies up to ~87%). These findings suggest that synchrony-aware EEG features can objectively assess simultaneous flow in cooperative contexts, laying groundwork for real-time adaptive multi-user systems and advancing the understanding of team flow dynamics.

Abstract

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.

Figures (5)

• Figure 1: Screenshots of the collaborative game interface for user 1 (a) and user 2 (b). The interface displays various parameters related to team tasks, individual performance, and team performance in real-time.
• Figure 2: During the task, flow experiences are sampled at intervals by popping out an evaluation interface (a). Following the sampling, the game difficulty can be adjusted according to the two player’s skill by popping out an adjustment interface (b).
• Figure 3: Data acquisition apparatuses and experimental environment.
• Figure 4: Electrode placement distribution for EEG data acquisition.
• Figure 5: Process Diagram of Simultaneous Flow Collection System.