EEG Time–Frequency Image Encoding with Convolutional Networks for Alzheimer’s and FTD Classification
This study presents a deep learning framework that leverages EEG-derived time–frequency images and convolutional neural networks (CNNs) for robust, non-invasive classification of dementia subtypes, and consistently outperforms all baseline methods across accuracy, precision, recall, and F1-score.