Bimodal Transformer with Regional EEG Data for Accurate Gameplay Regularity Classification.

Abstract

As games have been applied across various fields, including education and healthcare, numerous new games tailored to each field have emerged. Therefore, understanding user behavior has become crucial in securing the right players for each type of game. This study provides valuable insights for improving game development by measuring the electroencephalography (EEG) of game users and classifying the frequency of game usage. The multimodal mobile brain-body imaging (MOBI) dataset was employed for this study, and the frequency of game usage was categorized into "often" and "sometimes". To achieve decent classification accuracy, a novel bimodal Transformer architecture featuring dedicated channels for the frontal (AF) and temporal (TP) lobes is introduced, wherein convolutional layers, self-attention mechanisms, and cross-attention mechanisms are integrated into a unified model. The model, designed to differentiate between AF and TP channels, exhibits functional differences between brain regions, allowing for a detailed analysis of inter-channel correlations. Evaluated through five-fold cross-validation (CV) and leave-one-subject-out cross-validation (LOSO CV), the proposed model demonstrates classification accuracies of 88.86% and 85.11%, respectively. By effectively classifying gameplay frequency, this methodology provides valuable insights for targeted game participation and contributes to strategic efforts to develop and design customized games for player acquisition.