In the present work, we used the brain electroencephalografic activity as an alternative means to identify individuals. 50 healthy subjects participated to the study and 56 EEG signals were recorded through a high-density cap during one minute of resting state either with eyes open and eyes closed. By computing the power spectrum density (PSD) on segments of 10 seconds, we obtained a feature vector of 40 points, notably the PSD values in the standard frequency range (1-40 Hz), for each EEG channel. By using a naive Bayes classifier and K-fold cross-validations, we observed high correct recognition rates (CRR) at the parieto-occipital electrodes (∼78% during eyes open, ∼89% during eyes closed). Notably, the eyes closed resting state elicited the highest CRRs at the occipital electrodes (92% O2, 91% O1), suggesting these biometric characteristics as the most suitable, among those investigated here, for identifying individuals. © 2011 IEEE.
Subject identification through standard EEG signals during resting states / DE VICO FALLANI, Fabrizio; Vecchiato, Giovanni; Toppi, Jlenia; Astolfi, Laura; Babiloni, Fabio. - 2011:(2011), pp. 2331-2333. (Intervento presentato al convegno 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 tenutosi a Boston, MA nel 30 August 2011 through 3 September 2011) [10.1109/iembs.2011.6090652].
Subject identification through standard EEG signals during resting states
DE VICO FALLANI, FABRIZIO;VECCHIATO, GIOVANNI;TOPPI, JLENIA;ASTOLFI, LAURA;BABILONI, Fabio
2011
Abstract
In the present work, we used the brain electroencephalografic activity as an alternative means to identify individuals. 50 healthy subjects participated to the study and 56 EEG signals were recorded through a high-density cap during one minute of resting state either with eyes open and eyes closed. By computing the power spectrum density (PSD) on segments of 10 seconds, we obtained a feature vector of 40 points, notably the PSD values in the standard frequency range (1-40 Hz), for each EEG channel. By using a naive Bayes classifier and K-fold cross-validations, we observed high correct recognition rates (CRR) at the parieto-occipital electrodes (∼78% during eyes open, ∼89% during eyes closed). Notably, the eyes closed resting state elicited the highest CRRs at the occipital electrodes (92% O2, 91% O1), suggesting these biometric characteristics as the most suitable, among those investigated here, for identifying individuals. © 2011 IEEE.File | Dimensione | Formato | |
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