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

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.