FEATURE EXTRACTION FOR EEG BASED EMOTION PREDICTION APPLICATIONS THROUGH CHAOTIC ANALYSIS

19th National Biomedical Engineering Meeting (BIYOMUT) -- NOV 05-06, 2015 -- -- Istanbul, TURKEY
WOS: 000380507300012
Electroencephalogram (EEG)-based emotion recognition is a rapidly growing field. This paper presents a research study on identifying the changes caused by human emotions in brain signals through EEG records and further examines the availability of chaotic analysis for emotion prediction applications. We designed an efficient presentation including 30 pictures from International Affective Picture System (IAPS) which could stimulate the emotions of happiness, sadness and fear. A group of 20 persons consisting of 12 males and 8 females took place in our research study as subjects voluntarily. In order to acquire the EEG signals under picture induction environment, a total of 30 pictures that might reveal the feelings of happiness, sadness and fear were shown to the volunteer subjects that participated in the study voluntarily. Data acquired from EEG records were analyzed by using chaotic analysis through MATLAB program and two different attributes consisting of "Largest Lyapunov Exponent (LLE)" and "Correlation Dimension" were obtained. The mean values of each attribute that obtained through chaotic analysis were compared by using independent t-test and dependent t-test in SPSS program with 95% confidence interval and a P value of p