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EEG fingerprinting: Subject-specific signature based on the aperiodic component of power spectrum
Open accessAbstract
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.
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“Segments of EEG from one subject are more similar to each other than to segments from different subjects (Demuru and Fraschini, 2020).”
From Data leakage in deep learning studies of translational EEG (2024) · cites this paper as Demuru and Fraschini, 2020
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Cites
Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity
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Sorted by citations
Data leakage in deep learning studies of translational EEG
Brookshire, G., Kasper, J., Blauch, N. M., Wu, Y. C., Glatt, R., Merrill, D. A., Gerrol, S., Yoder, K. J., Quirk, C., & Lucero, C. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1373515 · Cited by 103
“Segments of EEG from one subject are more similar to each other than to segments from different subjects (Demuru and Fraschini, 2020).”
Background
Metadata
- Authors
- Demuru, M., & Fraschini, M.
- Affiliations
- Stichting Epilepsie Instellingen Nederland (NL)
- University of Cagliari (IT)
- Corresponding author
- Fraschini, Matteo
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 120
- Pages
- 103748
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W3000818859
- Cited by
- 82
- References
- 1
- Citations by year
- 2026: 15 · 2025: 11 · 2024: 11 · 2023: 16 · 2022: 14 · 2021: 13 · 2020: 2
- FWCI
- 6.06
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UNICA IRIS Institutional Research Information System (University of Cagliari) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Regione Autonoma della Sardegna
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Keywords
- subject identification, aperiodic component, EEG spectral power, interindividual variability, eyes-open and eyes-closed conditions
- MeSH terms
- Brain, Electroencephalography, Humans
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