Deep learning technology has demonstrated exceptional performance in areas such as image recognition and natural language processing due to its powerful nonlinear fitting capabilities and end-to-end learning advantages.

Deep learning techniques are being used for various EEG signal analysis tasks, including emotion recognition, motor imagery, and disease diagnosis. Researchers are exploring methods to extract meaningful biomarkers from EEG data for detecting conditions like depression.

Deep learning methods applied to EEG signals can be broadly categorized into two types: - **CNN-Based Methods:** These treat EEG signals as images and use convolutional kernels to extract features. However, they often overlook the relationships between different EEG channels. - **GNN-Based Methods:** These map EEG signals into graph-structured data, utilizing pre-calculated graph adjacency matrices to represent channel relationships. This approach accounts for spatial structural relationships among channels.

CNN-based algorithms primarily focus on feature extraction from EEG signals treated as images and do not fully consider the inter-channel relationships, potentially missing important spatial dynamics.

GNN-based algorithms enhance EEG analysis by mapping signals into graph-structured data, using adjacency matrices to represent the connections between different channels. This approach better captures the spatial relationships and can facilitate the extraction of cross-channel features.

Pre-calculated graph adjacency matrices are fixed and cannot dynamically reflect individual brain network differences or changes in network connections over time. This limitation can be significant given the dynamic nature of brain networks.

Brain networks can exhibit dynamic changes, such as variations during rapid eye movement (REM) sleep or differences in connectivity between depressed and healthy individuals. These temporal and individual variations in brain connectivity may impact the effectiveness of static graph models in capturing brain network dynamics.

Studies have shown that brain network connectivity, such as cross-hemisphere functional connectivity, can be abnormally reduced in patients with mental illnesses like depression. This suggests significant differences in brain networks between depressed and healthy individuals.

Dynamic changes in brain networks are crucial to consider because they reflect the real-time adjustments in brain connectivity, which can impact the accuracy of EEG signal analysis and the detection of neurological conditions.