Resource-efficient AI-driven adaptive feature fusion and channel selection for EEG-based neuropsychological disease detection

Abstract

The escalating global burden of neuropsychological disorders demands objective and automated assessment tools, as traditional diagnostic methods often rely on subjective expert interpretation. Electroencephalography (EEG) offers a promising solution due to its non-invasiveness, cost-effectiveness, and high temporal resolution for capturing neural dynamics. Given the importance of long-term monitoring in managing these conditions, there is a critical need to develop portable and user-friendly EEG-based devices. In this context, channel selection is essential for minimizing hardware complexity and enhancing portability. To address this, we propose a novel framework utilizing an Adam-optimized adaptive feature fusion mechanism. Key multi-domain features, including Wavelet Convolution Standard Deviation (WCSD), Wavelet Time–Frequency Convolution Standard Deviation (WTF-CSD), spectral entropy, and waveform factor, are extracted to characterize complex neural dynamics. An adaptive weighting mechanism, optimized via the Adam algorithm, integrates these features into a discriminative representation, which is then evaluated using five machine learning classifiers: Support Vector Machine (SVM), Naive Bayes (NB), Generalized Additive Model (GAM), K-Nearest Neighbors (KNN), and Decision Tree (DT). Leave-One-Out Cross-Validation (LOOCV) is performed on public datasets for Schizophrenia (SZ) and Alzheimer's Disease (AD). Ablation experiments confirm that the adaptive fusion mechanism contributes significantly to performance gains. Furthermore, cross-dataset generalization is validated by training on one AD dataset (AHEPA) and testing directly on another (Florida-Based) without retraining. These results demonstrate that this approach achieves impressive performance using single-channel EEG data with the DT classifier. Specifically, the P4 channel achieves 81.90% ± 2.29% accuracy for the SZ dataset, while the T5 channel achieves 84.62% ± 1.09% accuracy for the AD dataset (AHEPA). By integrating Adam optimization into feature fusion, the proposed method eliminates multi-channel dependencies and minimizes electrode requirements for data-limited scenarios. Consequently, it offers a practical solution for developing a portable EEG-based diagnostic system.