Berita BPI LIPI
oleh : , ; ; Poltak Sihombing; Roby Harlen Setiadi; Edi Mulyana
EEG recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable signal variations due to artifacts or recognizer-subject feedback. In this paper, we propose an emotion classification method based on backpropagation neural networks to classify the EEG-SSVEP signals which are induced by the four short movie stimuli. The average of 75% classification accuracy is achieved for sad, angry, happy, and calm emotions. The evidences that higher accuracy of the EEG emotion recognition system can be achieved with higher precision of the psychological emotion design is provided.