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.
