AN EEG EMOTION RECOGNITION METHOD BASED ON ADABOOST CLASSIFIER

At present, most of the EEG emotion recognition studies have taken all electric shocks or filtered electrodes as a feature and they are integrated (combined) with simple features that are extracted from other signals as a single classifier Emotional classification, but there are problems such as low efficiency and low accuracy. Aiming at this problem, this paper proposes an EEG emotion classification method based on AdaBoost classifier to optimize the algorithm, dividing the feature samples into different channels, and then using the genetic algorithm to identify the EEG emotion, and the algorithm achieves in Matlab. The experimental result shows that the average recognition rate of EEG is 90.8%, which is superior to the current single classifier and majority voting method, and it has better stability and generalization performance.