HEMAsNet: A Hemisphere Asymmetry Network Inspired by the Brain for Depression Recognition From Electroencephalogram Signals

Depression is a prevalent mental disorder that affects a significant portion of the global population. Despite recent advancements in EEG-based depression recognition models rooted in machine learning and deep learning approaches, many lack comprehensive consideration of depression's pathogenesis, leading to limited neuroscientific interpretability. To address these issues, we propose a hemisphere asymmetry network (HEMAsNet) inspired by the brain for depression recognition from EEG signals. HEMAsNet employs a combination of multi-scale Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) blocks to extract temporal features from both hemispheres of the brain. Moreover, the model introduces a unique ’Callosum- like' block, inspired by the corpus callosum's pivotal role in facilitating inter-hemispheric information transfer within the brain. This block enhances information exchange between hemispheres, potentially improving depression recognition accuracy. To validate the performance of HEMAsNet, we first confirmed the asymmetric features of frontal lobe EEG in the MODMA dataset. Subsequently, our method achieved a depression recognition accuracy of 0.8067, indicating its effectiveness in increasing classification performance. Furthermore, we conducted a comprehensive investigation from spatial and frequency perspectives, demonstrating HEMAsNet's innovation in explaining model decisions. The advantages of HEMAsNet lie in its ability to achieve more accurate and interpretable recognition of depression through the simulation of physiological processes, integration of spatial information, and incorporation of the Callosum- like block.