An Ensemble of Lightweight Convolutional Neural Networks for EEG-Based Major Depressive Disorder Detection

Major Depressive Disorder (MDD) is a prevalent mental illness and a leading cause of suicide, making early detection critical. The electroencephalogram (EEG) provides a non-invasive method for recording brain activity; however, current diagnostic methods rely on manual evaluation by neurologists, which can be subjective, labor-intensive, and time-consuming. While Machine Learning (ML) and Deep Learning (DL) have shown promise in medical diagnosis, many existing models for MDD detection lack generalizability due to a large number of learnable parameters, small datasets, and data leakage from Patient-Independent evaluation protocols. In this work, we propose an ensemble classifier of lightweight convolutional neural networks (CNNs) for MDD detection using EEG signals, designed with minimal learnable parameters to ensure efficiency and generalization. The method was evaluated on the MUMTAZ benchmark dataset using Leave-Some-Subject-Out Cross Validation (LSSOCV) to ensure robust performance, achieving an accuracy of 94.1%. This approach offers a practical tool to assist psychiatrists in the early and objective detection of MDD, supporting timely intervention.