Abstract: This paper discussed the feature selection from ECG signal in affective recognition. At first, the original fcatures with high correlation were deleted to reduce dimensionality of original feature set by correlation analysis. Andthen, an improved quantum-behaved particle swarm optimization with binary encoding algorithm was proposed to achieve effective feature selection in the feature space with reduced dimension. hhe experimental results shows that the affective recognition system based on this algorithm and fisher classifier recognize the anger,disgust,fear,grief,joy and surpnse successfully.

Key words: Feature selection, Correlation analysis, I3QPS0 algorithm, Affective recognition