EEG fingerprinting: Subject-specific signature based on the aperiodic component of power spectrum

During the last few years, there has been growing interest in the effects induced by individual variability on activation patterns and brain connectivity. The practical implications of individual variability are of basic relevance for both group level and subject level studies. The Electroencephalog...

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Bibliographic Details
Published in:Computers in biology and medicine Vol. 120; p. 103748
Main Authors: Demuru, Matteo, Fraschini, Matteo
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.05.2020
Elsevier Limited
Subjects:
Aperiodic component
Biometrics
Brain
Datasets
EEG
Electroencephalography
Feature extraction
Fingerprint
Fingerprinting
Frequencies
Internal Medicine
Neural networks
Other
Power spectra
Aperiodic component
Fingerprint
Power spectra
EEG
ISSN:0010-4825, 1879-0534, 1879-0534
Online Access:Get full text
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Summary:During the last few years, there has been growing interest in the effects induced by individual variability on activation patterns and brain connectivity. The practical implications of individual variability are of basic relevance for both group level and subject level studies. The Electroencephalogram (EEG), still represents one of the most used recording techniques to investigate a wide range of brain-related features. In this work, we aim to estimate the effect of individual variability on a set of very simple and easily interpretable features extracted from the EEG power spectra. In particular, in an identification scenario, we investigated how the aperiodic (1/f background) component of the EEG power spectra can accurately identify subjects from a large EEG dataset. The results of this study show that the aperiodic component of the EEG signal is characterized by strong subject-specific properties, that this feature is consistent across different experimental conditions (eyes-open and eyes-closed) and outperforms the canonically-defined frequency bands. These findings suggest that the simple features (slope and offset) extracted from the aperiodic component of the EEG signal are sensitive to individual traits and may help to characterize and make inferences at single subject-level. •We estimated the effect of individual variability on a set of EEG features.•The aperiodic components of EEG have strong subject-specific properties.•These features outperform the canonically defined frequency bands.
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2020.103748