EEG-Based Depression Classification and Brain Region Analysis Using a Hybrid of NeuCube and Dictionary Learning Framework

Abstract The Study of depression and its effects on the brain is essential since this common mental health disorder affects millions. In addition to disturbing emotional and cognitive processes, depression also disrupts activity in discrete brain regions. Identifying these distortions is important for expanding the diagnosis and treatment plans. A recently introduced spiking neural network (SNN) framework called NeuCube has successfully demonstrated its effectiveness in modeling dynamic brain activity using EEG signals by capturing the temporal and spatial patterns of neural activity. In this study, we introduce a new model to interpret brain region contributions in depression by integrating NeuCube architecture with a dictionary learning method. NeuCube artificially replicates neural activity in multiple brain regions and its output spike trains are then combined with dictionary learning to recognize depression-related patterns. This hybrid solution allows a high level of interpretability with respect to the contribution from biology and connectivity while maintaining strong separation power for depression diagnosis. Significant contributions were observed in neuromarkers of brain regions, such as frontal and temporal lobes for eyes-closed, and eye-open states. Through the analysis of sparse codes, we reveal which brain region interactions are affected by depression, providing an understanding of the neural underpinnings behind this disorder. In addition, our model achieves an accuracy of 91\% for both eyes-closed and eyes-open conditions, stronger than traditional methods.