Abstract

Emotion recognition by computers is becoming increasingly popular. Our project focused on recognizing emotion from human brain activity, measured by eeg signals. We have proposed a system to analyze eeg signals and classify them into 5 classes on two emotional dimensions, valence and arousal. This system was designed using prior knowledge from other research, and is meant to assess the quality of emotion recognition using eeg signals in practice.
In order to perform this assessment, we have gathered a dataset with eeg signals. This was done by measuring eeg signals from people that were emotionally stimulated by pictures. This method enabled us to teach our system the relationship between the characteristics of the brain activity and the emotion.
We found that the eeg signals contained enough information to separate ve dierent classes on both the valence and arousal dimension. However, using a 3-fold cross validation method for training and testing, we reached classication rates of 32% for recognizing the valence dimension on from eeg signals and 37% for the arousal dimension when. Much better classication rates were achieved when using only the extreme values on both dimensions, the rates were 71% and 81%.