An EEG-based cross-subject interpretable CNN for game player expertise level classification

• Published in: Expert systems with applications Vol. 237; p. 121658
• Main Authors: Lin, Liqi, Li, Pengrui, Wang, Qinghua, Bai, Binnan, Cui, Ruifang, Yu, Zhenxia, Gao, Dongrui, Zhang, Yongqing
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2024
• Subjects: Cross-subject; Depthwise separable convolution; Electroencephalography (EEG); Game player; Interpretable technology; Electroencephalography (EEG); Depthwise separable convolution; Cross-subject; Interpretable technology; Game player
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.121658

Electroencephalogram (EEG) signals have been demonstrated to be an effective method for game player expertise level classification, as it can reflect the activity state of the player’s brain during the game task. Although many efforts have been made to classify the expertise level of game players based on EEG signals, existing methods still need to be improved in identifying common brain patterns across different subjects. In this article, we propose a Spatiotemporal-Based Brain Pattern Recognition Network (BPR-STNet), which uses depthwise separable convolution to extract the spatiotemporal features of EEG signals and incorporates Gradient-weighted Class Activation Mapping (Grad-CAM) interpretable technology to explore common brain patterns among different subjects. To evaluate the model’s performance, we use non-invasive wearable devices to collect EEG signals from 19 subjects during the game process. The results of the leave-one-out cross-subject experiments demonstrate that the average accuracy of the model’s game player expertise level classification is 82%–86.32% in the five frequency bands (δ, θ, α, β, γ). Among them, the γ frequency band has the highest accuracy of 86.32%. More importantly, the average accuracy of our method is 1.56%–5.79% higher than the state-of-the-art deep learning methods. Moreover, interpretable results indicate the model can learn biologically significant features from EEG frequency bands. Overall, this study can offer a new idea of using deep learning to explore common brain patterns and features among different subjects at the same level. The BPR-STNet code is available at https://github.com/L000077/BPR-STNet. •Propose convolutional model for spatiotemporal feature learning.•γ band outperforms other frequency bands for classification.•Parietal and temporal regions may be common features among experts.