Explainable fMRI-based brain decoding via spatial temporal-pyramid graph convolutional network.

ABSTRACT: Brain decoding, aiming to identify the brain states using neural activity, is important for cognitive neuroscience and neural engineering. However, existing machine learning methods for fMRI-based brain decoding either suffer from low classification performance or poor explainability. Here, we address this issue by proposing a biologically inspired architecture, Spatial Temporal-pyramid Graph Convolutional Network (STpGCN), to capture the spatial-temporal graph representation of functional brain activities. By designing multi-scale spatial-temporal pathways and bottom-up pathways that mimic the information process and temporal integration in the brain, STpGCN is capable of explicitly utilizing the multi-scale temporal dependency of brain activities via graph, thereby achieving high brain d

SUBMITTER: Ye Z 

PROVIDER: S-EPMC10089104 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature