Methods to highlight consistency in repeated EEG recordings

Abstract

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 cross-validations, 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.