Affective signal processing algorithms were developed to allow a digital computer to recognize the affective state of a user who is intentionally expressing that state. This paper describes the method used for collecting the training data, the feature extraction algorithms used and the results of pattern recognition using a Fisher linear discriminant and the leave one out test method. Four physiological signals, skin conductivity, blood volume pressure, respiration and an electromyogram (EMG) on the masseter muscle were analyzed. It was found that anger was well differentiated from peaceful emotions (90%-100%), that high and low arousal states were distinguished (80%-88%), but positive and negative valence states were difficult to distinguish (50%-82%). Subsets of three emotion states could be well separated (75%-87%) and characteristic patterns for single emotions were found.
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