Automatic Detection of Emotional State from EEG Signal by Gamma Coherence Approach

Electroencephalogram coherence analysis is an important measure to help us to assess functional cortical connections and to learn about regional cortical synchronization. In this study, it was aimed to automatically detect emotions related to audio-visual stimuli by electroencephalogram coherence approach. First, the synchronizations of EEG recorded from different regions of the scalp have been analyzed with each other. Coherence analysis was performed for the gamma band of the electroencephalogram signals. Electrode pairs were identified in which the changing emotional state can be observed clearly. The coherence features extracted from the electrode pairs were given to input of the classifier algorithm. The average classification accuracy for the four different participants was obtained as 83.5%.