From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification

Johannes Wagner, Jonghwa Kim and Elisabeth Andre

erschienen 2005 "IEEE International Conference on Multimedia & Expo (ICME 2005)", Pages 940 - 943

Verlag: IEEE Computer Society

ISBN: 0-7803-9331-7 DOI: IEEE Computer Society


Little attention has been paid so far to physiological signals for emotion recognition compared to audio-visual emotion channels, such as facial expressions or speech. In this paper, we discuss the most important stages of a fully implemented emotion recognition system including data analysis and classification. For collecting physiological signals in different affective states, we used a music induction method which elicits natural emotional reactions from the subject. Four-channel biosensors are used to obtain electromyogram, electrocardiogram, skin conductivity and respiration changes. After calculating a sufficient amount of features from the raw signals, several feature selection/reduction methods are tested to extract a new feature set consisting of the most significant features for improving classification performance. Three well-known classifiers, linear discriminant function, k-nearest neighbour and multilayer perceptron, are then used to perform supervised classification.