EEG-based multi-scale perturbed brain cognitive pattern recognition network for gamer level classification

Electroencephalogram (EEG)-based classification of video game experts versus amateurs reveals cognitive brain patterns underlying complex abilities, with applications in attention modulation, medical rehabilitation, and health monitoring. While deep learning has advanced EEG-based cognitive state detection, challenges remain in extracting meaningful patterns from noisy data and preventing reverse inference attacks on user privacy. Here, we propose a novel multi-scale perturbed brain cognitive pattern recognition network (MsPE). Its key contributions are: (1) a multi-scale weak encryption method with attention mechanisms that protects privacy by perturbing EEG signals in temporal and frequency domains; (2) ConvFormer modules with adaptive channel sizes (3, 5, 15) and attention fusion to generate personalized perturbations while preserving task-relevant information; (3) a Denoise Feature Extraction Block (DFEB) using deep separable CNNs with skip connections to extract spatio-temporal features and reduce noise. Validated on a gaming EEG dataset, MsPE achieves 88.75% accuracy, 90.27% recall, 85.11% specificity, an F1 score of 0.8923, and a Kappa coefficient of 0.6957, outperforming existing methods. Interpretability analysis reveals distinct cognitive patterns between experts and amateurs in the temporal, occipital, and frontal lobes, with the most pronounced differences in the frontal lobe. This study advances an effective, secure, and accurate EEG-based cognitive pattern analysis solution. • Multi-scale encryption protects EEG privacy and preserves task features. • ConvFormer-based MsPE enables personalized EEG signal perturbation via adaptive channels. • DFEB extracts spatio-temporal features from perturbed EEG via separable CNNs. • Achieved superior 88.75% accuracy in gamer expertise classification.