EEG-based emotion recognition using hybrid filtering and higher order crossings

EEG-based emotion recognition is a relatively new research field in the human computer interaction area and its aim is the implementation of new algorithms that would identify and recognize emotions from EEG (electroencephalogram) signals. Towards that, a novel method is presented in this paper that employs an optimized hybrid filter, using empirical mode decomposition (EMD) and genetic algorithms (GA), in order to isolate the intrinsic mode functions (IMFs) corresponding to the plurality of the energy content of the initial signal for classification. The filtered signal is constructed by the selected IMFs and is subjected to higher order crossings (HOC) analysis for feature extraction. The final feature vector is classified into six emotion classes, i.e., happiness, anger, fear, disgust, sadness, and surprise, using quadratic discriminant analysis. The high classification performance (84.72% maximum mean classification rate) justifies the efficiency of the proposed EEG-based emotion recognition approach.