An emotion EEG recognition method providing emotion recognition model time robustness, comprising: performing pre-processing on a collected 64-lead EEG signal comprising changing a reference to a binaural average, downsampling to 500 Hz, performing 1-100 Hz bandpass filtering, and using an independent component analysis algorithm to remove EOG interference; finding an optimal discriminative frequency component in a pre-processed EEG signal by means of adaptive tracking of discriminative frequency components, and calculating a power spectral density of the optimal discriminative frequency component on each lead, respectively, forming an emotion characteristic matrix; using principal component analysis to perform dimension reduction on the characteristic matrix; using a support vector machine classifier to perform recognition on the dimension-reduced EEG power spectrum characteristics, establishing an emotion recognition model. The described solution finds an optimal discriminative frequency component by means of adaptive tracking of discriminative frequency components, strengthens emotion correlation characteristics by means of increasing training set sample days in an emotion recognition model, weakens a time specificity characteristic, and increases time robustness of an emotion recognition model.
EMOTION EEG RECOGNITION METHOD PROVIDING EMOTION RECOGNITION MODEL TIME ROBUSTNESS