The neural oscillatory mechanism underlying human brain fingerprint recognition using a portable EEG acquisition device.

Publisher: Academic Press Country of Publication: United States NLM ID: 9215515 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-9572 (Electronic) Linking ISSN: 10538119 NLM ISO Abbreviation: Neuroimage Subsets: MEDLINE

Original Publication: Orlando, FL : Academic Press, c1992-

Electroencephalography*/instrumentation ; Electroencephalography*/methods; Humans ; Male ; Female ; Adult ; Young Adult ; Alpha Rhythm/physiology ; Brain/physiology ; Prefrontal Cortex/physiology

In recent years, brainprint recognition has emerged as a novel method of personal identity verification. Although studies have demonstrated the feasibility of this technology, some limitations hinder its further development into the society, such as insufficient efficiency (extended wear time for multi-channel EEG cap), complex experimental paradigms (more time in learning and completing experiments), and unclear neurobiological characteristics (lack of intuitive biomarkers and an inability to eliminate the impact of noise on individual differences). Overall, these limitations are due to the incomplete understanding of the underlying neural mechanisms. Therefore, this study aims to investigate the neural mechanisms behind brainwave recognition and simplify the operation process. We recorded prefrontal resting-state EEG data from 40 participants, which is followed up over nine months using a single-channel portable brainwave device. We found that portable devices can effectively and stably capture the characteristics of different subjects in the alpha band (8-13Hz) over long periods, as well as capturing their individual differences (no alpha peak, 1 alpha peak, or 2 alpha peaks). Through correlation analysis, alpha-band activity can reveal the uniqueness of the subjects compared to others within one minute. We further used a descriptive model to dissect the oscillatory and non-oscillatory components in the alpha band, demonstrating the different contributions of fine oscillatory features to individual differences (especially amplitude and bandwidth). Our study validated the feasibility of portable brainwave devices in brainwave recognition and the underlying neural oscillation mechanisms. The fine characteristics of various alpha oscillations will contribute to the accuracy of brainwave recognition, providing new insights for the development of future brainwave recognition technology.
Competing Interests: Declaration of competing interest S.H., J.M., F.W., Y.L., and W.C. are employees of Shenzhen Shuimu AI Technology Co., Ltd. The authors declare that no other competing interests exist. The other authors declare no competing financial conflicts of interest to disclose.
(Copyright © 2024. Published by Elsevier Inc.)

Keywords: Alpha oscillation; Brain fingerprinting; EEG; Portable acquisition

Date Created: 20240507 Date Completed: 20240529 Latest Revision: 20240529

20240530

10.1016/j.neuroimage.2024.120637

38714216