This paper investigates the potential of physiological signals as a reliable channel for automatic recognition of user's emotial state. For the emotion recognition, little attention has been paid so far to physiological signals compared to audio-visual emotion channels such as facial expression or speech. All essential stages of automatic recognition system using biosignals are discussed, from recording physiological dataset up to feature-based multiclass classification. Four-channel biosensors are used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to search the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by emotion recognition results. © 2008 Springer-Verlag.
CITATION STYLE
Kim, J., & André, E. (2008). Four-channel biosignal analysis and feature extraction for automatic emotion recognition. In Communications in Computer and Information Science (Vol. 25 CCIS, pp. 265–277). https://doi.org/10.1007/978-3-540-92219-3_20
Mendeley helps you to discover research relevant for your work.