Turkish Journal of Electrical Engineering and Computer Sciences
Abstract
The objective of the study is to classify electroencephalogram signals recorded in a familiar and unfamiliar face recognition experiment. Frontal views of familiar and unfamiliar face images were shown to 10 volunteers in different sessions. In contrast to previous studies, no marker button was used during the experiment. Participants had to decide whether the displayed face was familiar or unfamiliar at the instant of stimulus presentation. The signals were analyzed in the preprocessing, channel selection, feature extraction, and classification stages. The novel two-feature extraction and eight-channel selection methods were applied to the analyses. Sixteen classification results were compared and the best performance was investigated. Consequently, the highest average classification accuracy was obtained at 72.67% when piecewise constant modeling feature extraction and relative entropy channel selection methods were used.
DOI
10.3906/elk-1608-130
Keywords
Electroencephalogram, evoked potential, familiar and unfamiliar face stimuli, feature extraction, channel selection, classification
First Page
3342
Last Page
3354
Recommended Citation
ÖZBEYAZ, ABDURRAHMAN and ARICA, SAMİ
(2017)
"Classification of EEG signals of familiar and unfamiliar face stimuli exploiting most discriminative channels,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 25:
No.
4, Article 64.
https://doi.org/10.3906/elk-1608-130
Available at:
https://journals.tubitak.gov.tr/elektrik/vol25/iss4/64
Included in
Computer Engineering Commons, Computer Sciences Commons, Electrical and Computer Engineering Commons