Methods to highlight consistency in repeated EEG recordings

P. Cserti, B. Végso, G. Kozmann, Z. Nagy, F. De Vico Fallani, F. Babiloni

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In the present work, we aimed to find subject related features in EEG recordings which are consistent through multiple recordings and apply them in biometry. Essentially to use the brain's electroencephalographic activity as a possible way to identify individuals. Seventeen healthy subjects participated in the study and their brain activity were recorded through a 56 EEG channel, high-density EEG cap during one minute of resting state with eyes open and/or eyes closed. The subjects were participating in a second recording session as well, thus creating a dataset of ten closed and ten open eyed recordings each with follow-up measurements. Analyzing results of various testing scenarios involving power spectrum density (PSD), coherence (COH),and the imaginary part of coherence (iCOH) on segments of ten seconds, we concluded the best parameter setup as well as a minimal set of electrodes and the best possible feature vector assembly based on these computations. By using a naive Bayes classifier and K-fold crossvalidations, we observed the highest correct recognition rates (CRR 98.33%) during eyes closed resting state at the parieto-occipital-temporal electrodes, suggesting these as the most stable characteristics therefore the most suitable, among those investigated here, for identifying individuals.

Original languageEnglish
Title of host publicationIFAC Proceedings Volumes (IFAC-PapersOnline)
Number of pages5
Publication statusPublished - 2012
Event8th IFAC Symposium on Biological and Medical Systems, BMS 2012 - Budapest, Hungary
Duration: Aug 29 2012Aug 31 2012


Other8th IFAC Symposium on Biological and Medical Systems, BMS 2012


  • Biometrics
  • Closed-eyes
  • EEG
  • Naïve Bayes classifier
  • Open-eyes
  • Resting state

ASJC Scopus subject areas

  • Control and Systems Engineering


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