EEG Fingerprints Research Articles

How Time Window Influences Biometrics Performance: An EEG-Based Fingerprint Connectivity Study

EEG-based biometrics represent a relatively recent research field that aims to recognize individuals based on their recorded brain activity using electroencephalography (EEG). Among the numerous features that have been proposed, connectivity-based approaches represent one of the more promising methods tested so far. In this paper, using the phase lag index (PLI) and the phase locking value (PLV) methods, we investigate how the performance of a connectivity-based EEG biometric system varies with respect to different time windows (using epochs of different lengths ranging from 0.5 s to 12 s with a step of 0.5 s) to understand if it is possible to define the optimal duration of the EEG signal required to extract those distinctive features. All the analyses were performed on two freely available EEG datasets, including 109 and 23 subjects, respectively. Overall, as expected, the results have shown a pronounced effect of the time window length on the biometric performance measured in terms of EER (equal error rate) and AUC (area under the curve), with an evident increase in the biometric performance as the time window increases. Furthermore, our initial findings strongly suggest that enlarging the window size beyond a specific maximum threshold fails to enhance the performance of biometric systems. In conclusions, we want to highlight that EEG connectivity has the potential to represent an optimal candidate as an EEG fingerprint and that, in this context, it is essential to establish an adequate time window capable of capturing subject-specific features. Furthermore, we speculate that the poor performance obtained with short time windows mainly depends on the difficulty of correctly estimating the connectivity metrics from very small EEG epochs (shorter than 8 s).

Towards a universal and privacy preserving EEG-based authentication system

EEG-based authentication has gained much interest in recent years. However, despite its growing appeal, there are still various challenges to their practical use, such as lack of universality, lack of privacy-preserving, and lack of ease of use. In this paper, we have tried to provide a model for EEG-based authentication by focusing on these three challenges. The proposed method, employing deep learning methods, can capture the fingerprint of the users’ EEG signals for authentication aim. It is capable of verifying any claimed identity just by having a genuine EEG fingerprint and taking a new EEG sample of the user who has claimed the identity, even those who were not observed during the training. The role of the fingerprint function is similar to the hash functions in password-based authentication and it helps preserve the user’s privacy by storing the fingerprint, rather than the raw EEG signals. Moreover, for targeting the lack of ease of use challenge, Gram-Schmidt orthogonalization process reduces the required number of channels to just three ones. The experiments show that the proposed method can reach around 98% accuracy in the authentication of completely new users with only three channels of Oz, T7, and Cz.

The Fingerprint-Like Pattern of Nocturnal Brain Activity Demonstrated in Young Individuals is Also Present in Senior Adulthood.

PurposeThe quantitative sleep EEG has been considered as electroencephalographic “fingerprint”, ie, it is stable within but differs between individuals. So far, however, almost all studies addressing this aspect have been conducted in young men. It was therefore of interest to know whether the sleep EEG fingerprint concept holds true in older samples of both sexes.Patients and MethodsData from three different subsamples of 30 healthy individuals each were reused for the present secondary analysis (young men (YM) = 25.6 ± 2.4 years, elderly men (EM) = 69.1 ± 5.5 years, elderly women (EW) = 67.8 ± 5.7 years). Individuals slept ten times in the sleep laboratory, resulting in a total of 900 study nights. However, to avoid misinterpretation due to intervention-related changes in sleep EEG power spectra, only the 3 sham nights without any intervention were included, reducing the datasets to 270. To determine stability of NREM sleep EEG power spectra between sham night pairs, within- and between-subject Manhattan distance measures were computed separately by sample.ResultsRegardless of subsample and sham night pair, lowest distance measures, ie, largest similarity, were observed for within-subject power spectra comparisons (range of mean distance measures for EW from 3.82 to 4.06, for EM from 3.55 to 3.63, and for YM from 3.04 to 3.62). Moreover, intraindividual similarity did not differ substantially between samples. Between-subject power spectra distance measures were considerably larger (range of mean distance measures for EW from 12.95 to 13.15, for EM from 12.21 to 12.57, and for YM from 10.33 to 10.78) and varied significantly between young and elderly individuals.ConclusionThe present results support the view that the sleep EEG power spectrum is an individual trait-like characteristic that remains unique up until old age. This finding may help to increase the sensitivity in measuring intervention effects.

Modification of brain waves and sleep parameters by Citrus reticulata Blanco. cv. Sai-Nam-Phueng essential oil

BackgroundCitrus essential oil (EO) has been used for mood elevation and sedative hypnotic purposes. However, scientific proofs of its central nervous system (CNS) action remained largely unexplored. This study investigated chemotypes, electrical brain waves and sleep-wake effects of the essential oil from Citrus reticulata in rat model. MethodsChemical contents of citrus EO were analyzed using gas chromatography-mass spectrometry (GC–MS). Male Wistar rats implanted with electrodes on the frontal and parietal skulls were used for electroencephalographic (EEG) recording while inhaling the citrus EO (200 μl on cotton wool). Diazepam (10 mg/kg, p.o.) was used as a standard anxiolytic drug. EEG frequency analyses were performed by using Fast Fourier transform. All data were statistical analyzed using One-way ANOVA followed by Tukey's test. ResultsGC–MS analysis revealed d-limonene (95.7%) as a major constituent of citrus EO. The EEG results showed that overall EEG patterns of citrus EO effects were relatively similar to that of diazepam. However, significant differences between treatments were seen from sleep-wake analyses. Diazepam significantly increased episode numbers of awake and non-rapid eye movement (REM) sleep and reduced averaged episode duration. On the other hand, the citrus EO significantly decreased REM sleep latency and increased total time and episode numbers of REM sleep. ConclusionThese findings demonstrated unique CNS effects of C. reticulata EO with EEG fingerprints and sleep-wake profiles. The data might be useful for citrus essential oil sub-classification and clinical application.

EEG Fingerprints: Phase Synchronization of EEG Signals as Biomarker for Subject Identification

The goal of biometrics is to recognize humans based on their physical and behavioral characteristics. Preliminary studies have demonstrated that the electroencephalogram(EEG) is potentially more secure and private than traditional biometric identifiers. At present, the EEG identification method targets specific tasks and cannot be generalized. In this study, a novel EEG-based biometric identification method that extracts the phase synchronization (PS) features for subject identification is proposed under a variety of tasks. We quantified the PS features by the phase locking value (PLV) in different frequency bands. Subsequently, we employed the principal component analysis (PCA) to reduce the dimension. Then, we used the linear discriminant analysis (LDA) to construct a projection space and projected the features onto the projection space. Finally, a feature vector was assigned to the class label. The experimental results of the proposed method used on 3 datasets with different cognitive tasks showed high classification accuracies and relatively good stabilities. From the results, we found that particularly in the beta and gamma bands, the average accuracies are more than 97% with the standard deviation equal to or less than the magnitude 10e-2 for both Dataset 1 and Dataset 2. For Dataset 3, the PS feature vectors in all off the bands have high classification accuracies, which are more than 97% with the standard deviation of the same magnitude. Our work demonstrated that the phase synchronization of EEG signals has task-free biometric properties, which can be used for subject identification.

The sleep EEG spectrum is a sexually dimorphic marker of general intelligence

The shape of the EEG spectrum in sleep relies on genetic and anatomical factors and forms an individual “EEG fingerprint”. Spectral components of EEG were shown to be connected to mental ability both in sleep and wakefulness. EEG sleep spindle correlates of intelligence, however, exhibit a sexual dimorphism, with a more pronounced association to intelligence in females than males. In a sample of 151 healthy individuals, we investigated how intelligence is related to spectral components of full-night sleep EEG, while controlling for the effects of age. A positive linear association between intelligence and REM anterior beta power was found in females but not males. Transient, spindle-like “REM beta tufts” are described in the EEG of healthy subjects, which may reflect the functioning of a recently described cingular-prefrontal emotion and motor regulation network. REM sleep frontal high delta power was a negative correlate of intelligence. NREM alpha and sigma spectral power correlations with intelligence did not unequivocally remain significant after multiple comparisons correction, but exhibited a similar sexual dimorphism. These results suggest that the neural oscillatory correlates of intelligence in sleep are sexually dimorphic, and they are not restricted to either sleep spindles or NREM sleep.

Effects of an alkaloid-rich extract from Mitragyna speciosa leaves and fluoxetine on sleep profiles, EEG spectral frequency and ethanol withdrawal symptoms in rats

BackgroundMany antidepressants are effective in alleviating ethanol withdrawal symptoms. However, most of them suppress rapid eye movement (REM) sleep. Thus, development of antidepressants without undesirable side effects would be preferable. Previously, crude alkaloid extract from Mitragyna speciosa (MS) Korth was found to produce antidepressant activities. It was hypothesized that the alkaloid extract from MS may attenuate ethanol withdrawal without REM sleep disturbance. MethodsAdult male Wistar rats implanted with electrodes over the frontal and parietal cortices were used for two separated studies. For an acute study, 10 mg/kg fluoxetine or 60 mg/kg alkaloid extract from MS were administered intragastrically. Electroencephalographic (EEG) signals were recorded for 3 h to examine sleep profiles and EEG fingerprints. Another set of animal was used for an ethanol withdrawal study. They were rendered dependent on ethanol via a modified liquid diet (MLD) containing ethanol ad libitum for 28 days. On day 29, fluoxetine (10 mg/kg) or alkaloid extract from MS (60 mg/kg) were administered 15 min before the ethanol-containing MLD was replaced with an isocaloric ethanol-free MLD to induced ethanol withdrawal symptoms. ResultsThe sleep analysis revealed that alkaloid extract from MS did not change any REM parameters which included average duration of each REM episode, total REM time, number of REM episode and REM latency whereas fluoxetine significantly suppressed all REM parameters and delayed REM latency. However, power spectral analysis revealed similar fingerprints for fluoxetine and alkaloid extract from MS characterized by decreasing powers in the slow frequency range in frontal and parietal cortical EEG. Neither treatment affected spontaneous motor activity. Finally, both alkaloid extract from MS and fluoxetine were found to significantly attenuate ethanol withdrawal-induced hyperexcitability (increases gamma activity) in both cortices and to reduce locomotor activity. ConclusionThe present study demonstrated that the alkaloid extract from MS alleviates ethanol withdrawal severity with no side effect on REM sleep. In addition, these data suggest that suppressive effects on slow frequency powers but not REM sleep may be hallmarks of effective antidepressants for ethanol withdrawal treatment.

Sleep EEG fingerprints reveal accelerated thalamocortical oscillatory dynamics in Williams syndrome

Sleep EEG alterations are emerging features of several developmental disabilities, but detailed quantitative EEG data on the sleep phenotype of patients with Williams syndrome (WS, 7q11.23 microdeletion) is still lacking. Based on laboratory (Study I) and home sleep records (Study II) here we report WS-related features of the patterns of antero-posterior 8-16 Hz non-rapid-eye-movement (NREM) sleep EEG power distributions. Participants in Study I were 9 WS and 9 typically developing (TD) controls matched for age (14-29 years) and sex, and sleeping for two consecutive nights in the laboratory. WS participants were characterized by region-independent decreases in 10.50-12.50 Hz and central increases in 14.75-15.75 Hz EEG power. Region-independent decreases and increases in z-scores of the spectra were observed in the 10.25-12.25 Hz and 14-16 Hz ranges, respectively. Moreover, in the EEG spectra of participants with WS a lower probability for the emergence of a frontally dominant peak was observed. Parietal fast sigma peaks and the antero-posterior shifts in power distributions were of higher frequencies in WS (~1 Hz difference). A 1 year follow-up of 9 WS and 3 TD participants, as well as their inclusion into larger samples (20 WS and 20 TD, age: 6-29 years) of a two-night ambulatory home polysomnography study confirmed the WS-specific decrease in alpha/low sigma power (8-11.75 Hz) and the pattern of z-score differences (decreases: 8.50-11.25 Hz; increases: 13.5-14 Hz), including the antero-posterior shifts in power distribution (0.5 Hz) and some features of the spectral peaks. Altogether these data suggest a decrease in alpha/low sigma power, as well as a redistribution of NREM sleep 8-16 Hz EEG power toward the higher frequencies and/or a higher frequency of NREM sleep thalamocortical oscillations in WS.

Evidence for the acceleration of thalamocortical oscillatory dynamics in Williams syndrome: lessons from the EEG fingerprints of NREM sleep

Event Abstract Back to Event Evidence for the acceleration of thalamocortical oscillatory dynamics in Williams syndrome: lessons from the EEG fingerprints of NREM sleep Róbert Bódizs1*, Ferenc Gombos1 and Ilona Kovács1 1 Hungarian Academy of Science, Cognitive Science Research Group, HAS-Budapest Technical University, Hungary Alterations in macro- and microstructural characteristics of sleep are emerging features of several developmental disabilities, but quantitative EEG data on the sleep phenotype of adolescents and young adults with Williams Syndrome (WS, a genetically determined developmental disorder linked to a microdeletion in chromosome 7q11.23 and associated with mild to moderate mental retardation and with a distinctive cognitive-linguistic profile) is still lacking. Here we report WS-specific features of patterns of antero-posterior 8–16 Hz EEG power distributions during NREM sleep, which were formerly shown to be stable in time, resistant to experimental perturbations and characterized by strong genetic determination in adult volunteers. Subjects were 9 WS and 9 controls (C) matched for age and sex, sleeping for two consecutive nights in the laboratory. Analyses included group comparisons of raw and z-transformed power spectra, as well as the analysis of sleep spindling by the individual adjustment method. WS subjects were characterized by a region-independent decrease in high alpha/low sigma (10.50–12.50 Hz) and a central increase in high sigma (14.75–15.75 Hz) raw EEG power. Z-scores were characterized by decreased 10.25–12.25 Hz values and increased 14–16 Hz values in a region-independent manner, but with maximal effects at the fronto-centro-parietal derivations surrounding the vertex. Fast sleep spindling and the antero-posterior differences in power distributions occurred at higher frequencies in WS than in C subjects, the differences averaging a 1 Hz shift. These data suggest a redistribution of the 8–16 Hz EEG power toward the higher frequencies and/or a higher frequency of NREM sleep thalamocortical oscillatory activity in WS. This affords the possibility of analyzing the genetic bases of thalamocortical dynamics in humans and other mammals. Supported by the National Research Fund of Hungary (OTKA NF60806 to I.K.). Conference: IBRO International Workshop 2010, Pécs, Hungary, 21 Jan - 23 Jan, 2010. Presentation Type: Poster Presentation Topic: Disorders of the nervous system Citation: Bódizs R, Gombos F and Kovács I (2010). Evidence for the acceleration of thalamocortical oscillatory dynamics in Williams syndrome: lessons from the EEG fingerprints of NREM sleep. Front. Neurosci. Conference Abstract: IBRO International Workshop 2010. doi: 10.3389/conf.fnins.2010.10.00028 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 19 Apr 2010; Published Online: 19 Apr 2010. * Correspondence: Róbert Bódizs, Hungarian Academy of Science, Cognitive Science Research Group, HAS-Budapest Technical University, Budapest, Hungary, rbodizs@cogsci.bme.hu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Róbert Bódizs Ferenc Gombos Ilona Kovács Google Róbert Bódizs Ferenc Gombos Ilona Kovács Google Scholar Róbert Bódizs Ferenc Gombos Ilona Kovács PubMed Róbert Bódizs Ferenc Gombos Ilona Kovács Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.