Multidomain Electroencephalography Biomarker Fusion for Machine Learning-Based Dementia Classification

Electroencephalography (EEG) has been investigated as a noninvasive approach for characterizing brain activity in neurodegenerative conditions. This study evaluated whether multidomain EEG biomarkers could distinguish Alzheimer's disease (AD), frontotemporal dementia (FTD), and healthy controls (HC). The publicly available dataset contained resting-state, eyes-closed EEG recordings from 88 participants, including 36 with AD, 23 with FTD, and 29 healthy controls. Multidomain EEG biomarkers and demographic features were extracted for machine learning classification. The EEG-derived features included root-mean-square (RMS), power spectral density (PSD), and entropy measures. A Random Forest classifier was trained using the combined feature set and interpreted using explainable artificial intelligence. Feature-stability and Cohen's d effect-size analyses were performed to evaluate feature contributions, followed by an ablation analysis. Entropy-based features showed the lowest contribution to classification. The complete multidomain model achieved 96% training accuracy and 92% test accuracy. After the removal of the entropy features, test accuracy increased to 94%. These results describe the classification performance of the selected multidomain EEG and demographic features within the Random Forest framework.