Emotion Recognition Based on DEAP Database Physiological Signals

Recognizing and accurately classifying human emotion is a complex and challenging task. Recently, great attention is paid to the emotion recognition methods using three different approaches: based on non-physiological signals (like speech and facial expression), based on physiological signals or based on hybrid approaches. Non-physiological signals are easily controlled by the individual, so these approaches have downsides in real world applications. In this paper, an approach based on physiological signals which cannot be willingly influenced (electroencephalogram, heartrate, respiration, galvanic skin response, electromyography, body temperature) is presented. Publicly available DEAP database was used for the binary classification (high vs. low) considering four frequently used emotional parameters (arousal, valence, liking and dominance). We have extracted 1490 features from the dataset, reduced to less than 15% (200 most significant features) and applied three different classification approaches – Support Vector Machine, Boosting algorithms and Artificial Neural Networks.