Depression, a major mental health disorder, has been increasingly prevalent worldwide. The diagnosis of depression through multichannel EEG topology emerges as a promising research direction. So, we introduce a method named MGFormer, designed to explore the complex interactions among channels and unearth underlying patterns within topological structures. Specifically, we propose a channel’s information aggregation strategy. Leveraging the capabilities of graph convolutional networks combined with internal across receptive fields, this approach flexibly extracts the channel’s neighboring features and captures spatial information at varying propagation depths. Compared to traditional GNN-based methods, this mechanism overcomes the limitations of node information aggregation, and pays more attention to the personalized needs of each channel. To optimize this process, we employ a precomputation technique that facilitates the parallel acquisition of these features. Moreover, we develop an information fusion strategy based on the cross-attention Transformer to enhance the dynamic interaction between different modalities. By exchange of query vectors, the model enhances information integration. Our method is verified on the HUSM and MODMA datasets. The model’s accuracy reaches 99.46% and 91.67%, respectively. We observe that depressed individuals exhibit significant differences in frontal and temporal EEG patterns. This work underscores the contribution of multichannel spatiotemporal features in depression detection, offering valuable support for its auxiliary diagnosis.