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Huseyin Atasoy,Esen Yildirim 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.7
In this study, electroencephalography signals recorded while participants were doing verbal and quantitative tasks, are classified. A dataset containing 1044 records obtained from 18 participants are used for subject-dependent classifications. Features are derived from phase locking values calculated between all channel pairs. Features are reduced before the classification process by using both analysis of variance and correlation based feature selection methods. Instances in the dataset are classified by using the nearest neighbor algorithm. An average classification accuracy of 92.35% is achieved over 18 participants. It is shown that phase locking value is distinctive especially when it is calculated on delta and gamma frequency bands measured between frontal and occipital regions.