Investigating convolutional and transformer-based models for classifying Mild Cognitive Impairment using 2D spectral images of resting-state EEG

Alzheimer's disease (AD) is the most common form of dementia among the elderly, leading to significant cognitive and functional impairments. Mild Cognitive Impairment (MCI) serves as a transitional stage that may precede dementia, with some individuals remaining stable, some improving, and others progressing to various types of dementia, including AD. Electroencephalography (EEG) has emerged as a valuable tool for early monitoring and diagnosis of dementia. This paper addresses the challenge of MCI classification using EEG data by exploring the effectiveness of Convolutional Neural Networks (CNNs) and Transformer-based models. We introduce an innovative methodology for converting non-linear raw EEG recordings into suitable input images for deep learning networks. The dataset comprises EEG recordings from 10 MCI patients and 10 Healthy Control (HC) subjects. We utilize spectral images of scalograms, spectrograms, and their hybrid forms as input sets due to their effectiveness in recognizing transitions in non-stationary signals. Our results demonstrate that CNNs, transfer learning architectures, hybrid architectures, and the transformer-based Vision Transformer (ViT) method effectively classify these images. The highest performance rates were achieved with spectrogram images, yielding accuracy rates of 0.9927 for CNN and 0.9938 for ViT, with ViT exhibiting greater stability during training. While CNNs excel at capturing local pixel interactions, they overlook global relationships within images. This study provides a comprehensive exploration of EEG-based MCI classification, highlighting the potential impact of our findings on clinical practices for dementia classification. © 2025 Elsevier Ltd