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Decrypting the electrophysiological individuality of the human brain: Identification of individuals based on resting-state EEG activity


Valizadeh, Seyed Abolfazl; Riener, Robert; Elmer, Stefan; Jäncke, Lutz (2019). Decrypting the electrophysiological individuality of the human brain: Identification of individuals based on resting-state EEG activity. NeuroImage, 197:470-481.

Abstract

Biometric identification (BI) of individuals is a fast-growing field of research that is producing increasingly so-phisticated applications in several spheres of everyday life. Previous magnetic resonance imaging (MRI) studies have demonstrated that based on the high inter-individual variability of brain structure and function, it is possible to identify individuals with high accuracy. Otherwise, there is the common belief that electroencephalographic (EEG) data recorded at the surface of the scalp are too noisy for identification purposes with a comparably high hit rate. In the present work, we compared BI quality (F1-scores, accuracy, sensitivity, and specificity) between different types of functional (instantaneous, lagged, and total coherence, phase synchronization, correlation, and mutual information) and effective (Granger causality, phase synchronization, and coherence) connectivity mea-sures. Results revealed that across functional connectivity metrics, identification accuracy was in the range of 0.98–1, whereas sensitivity and F1-scores were between 0.00 and 1 and specificity was between 0.99 and 1. BI was higher for the connectivity metrics that are contaminated by volume conduction (instantaneous connectivity) compared to those that are unaffected by this variable (lagged connectivity). Support vector machine and neural network algorithms yielded the highest BI, followed by random forest and weighted k-nearest neighborhood, whereas linear discriminant analysis was less accurate. These results provide cross-validated counterevidence to the belief that EEG data are too noisy for identification purposes and demonstrate that functional and effective connectivity metrics are particularly suited for BI applications with comparable accuracy to MRI. Our results have important implications for fast, low-cost, and mobile BI applications.

Abstract

Biometric identification (BI) of individuals is a fast-growing field of research that is producing increasingly so-phisticated applications in several spheres of everyday life. Previous magnetic resonance imaging (MRI) studies have demonstrated that based on the high inter-individual variability of brain structure and function, it is possible to identify individuals with high accuracy. Otherwise, there is the common belief that electroencephalographic (EEG) data recorded at the surface of the scalp are too noisy for identification purposes with a comparably high hit rate. In the present work, we compared BI quality (F1-scores, accuracy, sensitivity, and specificity) between different types of functional (instantaneous, lagged, and total coherence, phase synchronization, correlation, and mutual information) and effective (Granger causality, phase synchronization, and coherence) connectivity mea-sures. Results revealed that across functional connectivity metrics, identification accuracy was in the range of 0.98–1, whereas sensitivity and F1-scores were between 0.00 and 1 and specificity was between 0.99 and 1. BI was higher for the connectivity metrics that are contaminated by volume conduction (instantaneous connectivity) compared to those that are unaffected by this variable (lagged connectivity). Support vector machine and neural network algorithms yielded the highest BI, followed by random forest and weighted k-nearest neighborhood, whereas linear discriminant analysis was less accurate. These results provide cross-validated counterevidence to the belief that EEG data are too noisy for identification purposes and demonstrate that functional and effective connectivity metrics are particularly suited for BI applications with comparable accuracy to MRI. Our results have important implications for fast, low-cost, and mobile BI applications.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic and Policlinic for Internal Medicine
04 Faculty of Medicine > Balgrist University Hospital, Swiss Spinal Cord Injury Center
06 Faculty of Arts > Institute of Psychology
08 Research Priority Programs > Dynamics of Healthy Aging
Dewey Decimal Classification:610 Medicine & health
Uncontrolled Keywords:Cognitive Neuroscience, Neurology
Language:English
Date:1 August 2019
Deposited On:27 May 2019 13:33
Last Modified:18 Jun 2019 06:42
Publisher:Elsevier
ISSN:1053-8119
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.neuroimage.2019.04.005
PubMed ID:30978497
Project Information:
  • : FunderSNSF
  • : Grant IDCRSII3_136249
  • : Project TitleResting states of the brain and state dependent information processing in health and disease
  • : FunderSNSF
  • : Grant ID320030_163149
  • : Project TitleDie neuronalen Grundlagen des absoluten Gehörs und der Ton-Farbsynästhesie: Zwei Seiten einer Medaille?

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