Fig. 1: Supervised behavior analysis framework based on hypergraph self-attention neural networks. | npj Parkinson's Disease

Fig. 1: Supervised behavior analysis framework based on hypergraph self-attention neural networks.

From: A spatiotemporal hypergraph self-attention neural networks framework for the identification and pharmacological efficacy assessment of Parkinson’s disease motor symptoms

Fig. 1

Left module: Standard behavior dataset construction via unsupervised clustering combined with manual verification. Middle: module: The model extracts high-order dynamic behavior features through spatial hyperedges and temporal hyperedges, ultimately outputting the probability distribution of behavior categories. Right module: After extracting the principal components of the behavior data, SVM classification is used to distinguish the behavioral features of mice from different experimental groups.

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