Comparative Evaluation of Advanced Neural Network Architectures for EEG-based Classification: A Comprehensive Analysis of Physics-Informed, Biology-Informed, Quantum, Koopman, Spiking, Binary, and Graph Neural Networks with Multi-Modal Neurofeedback

This research proposal presents a comprehensive comparative study evaluating seven cutting-edge neural network architectures for electroencephalogram (EEG) signal classification: Physics-Informed Neural Networks (PINNs), Biology-Informed Neural Networks (BINNs), Quantum Deep Learning (QDL), Koopman Operator Learning, Spiking Neural Networks (SNNs), Binary Neural Networks (BNNs), and Graph Neural Networks (GNNs). The study aims to determine the most effective approach for EEG-based brain state classification while providing personalized neurofeedback applications through blackbox, whitebox, and grey-box interpretability methods. We propose to employ rigorous k-fold cross-validation strategies and comprehensive performance metrics to ensure robust evaluation across diverse EEG datasets. The research will utilize the EEG visual stimuli dataset containing recordings from 10 participants, focusing on brain hemisphere states and frequency band relationships through deep learning optimization techniques. Statistical analysis will include ANOVA, post-hoc tests (Tukey's HSD, Bonferroni correction), effect size calculations (Cohen's d, eta-squared), and non-parametric alternatives (Kruskal-Wallis, Mann-Whitney U tests). Expected outcomes include identification of optimal architecture-task pairings, development of multi-modal neurofeedback protocols, statistical validation of performance differences, and advancement of brain-computer interface applications with enhanced interpretability frameworks.