An Optimized Graph-based Deep Learning Framework for Depression Detection Using EEG Signals

Depression is a serious psychiatric disorder that greatly impacts the quality of life and daily functioning of a person. Accurate diagnosis at the early stage is critical for success with intervention. Electroencephalography (EEG) offers a non-invasive technique to assess neurophysiological activity and is thus an important instrument for diagnosis of depression. Current EEG-based deep learning approaches are beset by high-dimensional data, poor feature selection, and poor classification performance owing to the nature of the EEG signal. To address these issues, we introduce EEGEffV2-SpikeNet a new framework for depression detection that combines statistical feature extraction with deep feature extraction through a Graph Convolutional Network (GCN) approach. The proposed model incorporates a new fusion of statistical feature extraction and GCN-based deep feature learning for the extraction of both spatial and temporal EEG features. The extracted features are then optimized by Modified Addax Optimization Algorithm (MAOA), which is a cutting-edge bio-inspired optimization algorithm for optimizing feature selection efficiency by discarding redundant information and improving classification accuracy. For depression classification, EfficientNetV2, Deep Belief Network (DBN) and a Spiking Neural Network (SNN) are utilized based on the computational efficiency of EfficientNetV2 and the biologically simulated processing of SNN for enhancing feature representation and decision-making. Experimental results on two standard EEG datasets validate the better performance of the model, achieving 98.74% accuracy on Dataset 1 and 97.88% accuracy on Dataset 2, outperforming baseline models like DBN, EfficientNet, and SNN. The results prove the framework's promise as a dependable tool for objective and early depression diagnosis, with clinical application and mental health monitoring implications.