Efficient Classification of Depression using EEG through Spectral Graph Analysis

In this article, we propose a new approach to classify individuals with or without depression using electroencephalogram (EEG) signals. The methodology involves transforming EEG signals into the frequency domain, enabling the identification of spectral markers and their representation through directed graphs. The resulting spectral tensor is then employed to train the neural network model ResNet-101 with a fast processing architecture, which is ideal for embedded systems. Our method achieves high accuracy (91.03%), precision (91.81%), recall (91.03%), and F1-Score (90.96%) rates on the MODMA dataset, utilizing a subset of three channels (Fp1, Fpz, and Fp2). Our results demonstrate the method’s suitability for applications in embedded systems with a fast prediction time of 0.820 seconds per sample and a training time of 0.226 seconds per sample. These findings suggest promising prospects for mobile applications, emphasizing the method’s potential for deployment in diverse clinical settings.