Graph empirical mode decomposition and multiscale feature extraction for EEG-based classification of Alzheimer's disease and frontotemporal dementia

In the multiclass task (AD, FTD, HC), the method reached 96.68% accuracy, demonstrating the efficacy of the proposed method for the identification of Alzheimer's disease and frontotemporal dementia. Compared to previous research using the same dataset, our approach has demonstrated improved performance, validating the effectiveness of graph-based multiresolution feature fusion for dementia classification using EEG signals.