Disclosed by the present invention are a GPDC graph convolutional neural network-based fatigue detection method, an apparatus, and a storage medium, wherein the method comprises: collecting initial brain electrical data; performing processing on the initial brain electrical data, and obtaining a first brain electrical signal; performing segmentation on the first brain electrical signal and computing a directed static network by means of generalized partial directed coherence; selecting important connection information from the directed static network, and, after performing binarization on the important connection information, obtaining a brain function matrix; constructing a directed brain function network by means of the brain function matrix; modeling extracted brain electrical features into graph signals by means of the directed brain function network; inputting a training graph signal into a graph convolutional neural network, performing network training and optimization, and obtaining a driver fatigue detection model; inputting a testing graph signal into the driver fatigue detection model, performing identification and detection, and obtaining a feedback result, thereby improving detection performance for a driving fatigued state.
GPDC GRAPH CONVOLUTIONAL NEURAL NETWORK-BASED FATIGUE DETECTION METHOD, APPARATUS, AND STORAGE MEDIUM