GL-LFGNN applies Liang-Kleeman causal information flow within a global-local dual-branch GNN architecture to reach 86.17% arousal and 86.71% valence accuracy on the MEEG dataset using only 37K parameters.
In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp
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GL-LFGNN:A Global-Local Dual-branch Causal Graph Neural Network Based on Liang-Kleeman Information Flow for EEG Emotion Recognition
GL-LFGNN applies Liang-Kleeman causal information flow within a global-local dual-branch GNN architecture to reach 86.17% arousal and 86.71% valence accuracy on the MEEG dataset using only 37K parameters.