Signal, Image and Video Processing

, Volume 12, Issue 6, pp 1181–1188 | Cite as

Familiar/unfamiliar face classification from EEG signals by utilizing pairwise distant channels and distinctive time interval

  • Abdurrahman ÖzbeyazEmail author
  • Sami Arıca
Original Paper


The aim of the study is to classify single trial electroencephalogram and to estimate active regions/locations on skull in unfamiliar/familiar face recognition task. For this purpose, electroencephalographic signals were acquired from ten subjects in different sessions. Sixty-one familiar and fifty-nine unfamiliar face stimuli were shown to the subjects in the experiments. Since channel responses are different for familiar and unfamiliar classes, the channels discriminating the classes were investigated. To do so, three distances and four similarity measures were employed to assess the most distant channel pairs between familiar and unfamiliar classes for a 1-s time duration; 0.6 s from the stimulus to 1.6 s in a channel selection process. It is experimentally observed that this time interval is maintaining the greatest distance between two categories. The electroencephalographic signals were classified using the determined channels and time interval to measure accuracy. The best classification accuracy was 81.30% and was obtained with the Pearson correlation as channel selection method. The most discriminative channel pairs were selected from prefrontal regions.


Familiar/unfamiliar face stimuli Electroencephalogram Event-related potentials Channel selection Classification 



This study was supported by The National Scientific Research Council of Turkey (TUBITAK)—Grant No. 2211-C and Adıyaman University’s Scientific Research Fund - Project Number: TIPBAP/2012-0008. The experiments were approved by a local ethics committee of the health sciences Institute of Adıyaman University. Ethics Committee Decision Number is 2012/07-2.3. All participants signed an informed consent.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical and Electronics Engineering, Faculty of EngineeringUniversity of AdiyamanAdiyamanTurkey
  2. 2.Department of Electrical and Electronics Engineering, Faculty of EngineeringÇukurova UniversityAdanaTurkey

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