Greedy-mRMR: An emotion recognition algorithm based on EEG using greedy algorithm

Electroencephalography (EEG)-based emotion recognition methods mostly adopt features as many as possible to achieve high performance. However, this is not feasible and flexible for portable or wearable helmet devices because of the limited computing resource and requirement of real-time performance. Aiming at improving EEG emotion recognition performance with features as few as possible, this paper firstly designed a feature called Relative Intensity Ratio Entropy (RIRE), then proposed a novel feature selection method based on greedy algorithm and Max-Relevance and Min-Redundancy(mRMR), termed as Greedy-mRMR. Greedy algorithm is adopted to take top features in the ranking list, and mRMR algorithm is used to select features that are most relevant and minimum redundant. Greedy-mRMR algorithm also uses dynamic termination threshold to guarantee recognition accuracy in each iteration. Experiments were carried on DEAP dataset. SVM classifier achieved average classification accuracy of 91.16% in valence and 91.70% in arousal, by extracting RIRE feature and selecting with Greedy-mRMR algorithm. Experimental results show that RIRE is suitable for EEG emotion recognition and Greedy-mRMR outperforms state-of-the-art methods.