3 accepted papers
Applying deep learning techniques to Electroencephalogram (EEG) data has shown great potential in the field of depression detection. However, existing EEG-based depression detection models face challenges: they struggle to capture the complex spatiotemporal dependencies and the complementary nature…
Deep learning and Convolutional Neural Networks (CNNs) have driven major transformations in diverse research areas. However, their limitations in handling low-frequency in-formation present obstacles in certain tasks like interpreting global structures or managing smooth transition images. Despite t…
Numerous evidence has demonstrated the pathophysiology of a number of mental disorders is intimately associated with abnormal changes of dysfunctional integration of brain network. Functional connectome (FC) exhibits a strong discriminative power for mental disorder identification. However, existing…