The invention discloses an electroencephalogram
emotion recognition method based on a hierarchical multi-
scale map neural network, and belongs to the technical field of emotion calculation and bio-
electricity signal processing. According to the method, structural innovation is carried out on the basis of a traditional graph neural network, a multi-scale graph
network model with layer-by-layer feature enhancement from local to global is constructed, and the core lies in optimization and improvement of graph structure modeling, a
feature fusion mechanism and global dependency capture capability. According to the model, local graph
convolution is combined with a random walk normalized matrix, and inter-
electrode spatial dependence modeling is enhanced; a virtual
brain region center and an SE channel attention mechanism are introduced into the mesoscale layer, and cross-
brain region emotional features are effectively extracted; a simplified Transform structure is adopted in a global layer to capture a long-distance cross-
time step feature relationship, and finally, the feature expression capability and robustness are improved in combination with a
diffusion generation network. According to the method, the model weight is kept, meanwhile, high-precision modeling of the complex emotion mode of the EEG
signal is achieved, the accuracy, stability and cross-subject generalization ability of
emotion recognition are remarkably improved, and the novel efficient
emotion recognition method suitable for the multi-source physiological
signal scene is provided.