Electroencephalogram subject identification: A review

Pozo-Baños, Marcos del; Alonso, Jesús B.; Ticay-Rivas, Jaime R.; Travieso, Carlos M.

doi:10.1016/j.eswa.2014.05.013

Cited by 109 publications

(69 citation statements)

References 72 publications

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“…Moving from network stability to identification, many different EEG features have been successfully employed for subject recognition [for an overview, see (Del Pozo-Banos et al 2014)]. However, only a limited number of studies have addressed the long-term permanence of these effects, relying instead on data from the same acquisition session(s) for both classifier training and testing.…”

Section: Discussionmentioning

confidence: 99%

Variability and stability of large-scale cortical oscillation patterns

Cox

Schapiro

Stickgold

2016

Preprint

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Individual differences in brain organization exist at many spatial and temporal scales, contributing to the substantial heterogeneity underlying human thought and behavior. Oscillatory neural activity is crucial for these behaviors, but how such rhythms are expressed across the cortex within and across individuals has not been thoroughly characterized. Combining electroencephalography (EEG) with representational similarity and multivariate classification techniques, we provide a systematic characterization of brain-wide activity across frequency bands and oscillatory features during rest and task performance. Results indicate that oscillatory profiles exhibit sizable group-level correspondences, indicating the presence of common templates of oscillatory organization. At the same time, we observed well-defined subject-specific network profiles that were discernible above and beyond the structure shared across individuals. These individualized patterns were sufficiently stable over time to allow successful classification of individuals several months later. Finally, our findings indicate that the network structure of rhythmic activity varies considerably across distinct oscillatory frequencies and features, suggesting the existence of multiple, parallel information processing streams embedded in distributed electrophysiological activity. Together, these findings affirm the richness of spatiotemporal EEG signals and emphasize the utility of multivariate network analyses for understanding the role of brain oscillations in physiology and behavior.

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Section: Discussionmentioning

confidence: 99%

Variability and stability of large-scale cortical oscillation patterns

Cox

Schapiro

Stickgold

2016

Preprint

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“…Electroencephalography (EEG)-based subject identification is a relatively new biometric modality, with high robustness prospects [1]. Despite the inconveniences associated with the recording method, EEG signals are extremely hard to reproduce and cannot be furtively captured at a distance [2].…”

Section: Introductionmentioning

confidence: 99%

“…Previous works in the field of psychology suggest that there are significant differences in the way individuals feel and express emotions [4], and have demonstrated that EEG signals are a highly individual characteristic [5], [6], [7]. However, the distance between two samples from the same individual may depend on the subject's condition and the context in which the signals were captured, in between other factors [1]. For example, the stress level of the user may produce alterations in the EEG signal [8].…”

Section: Introductionmentioning

confidence: 99%

On the Influence of Affect in EEG-Based Subject Identification

Arnau-González

Arevalillo‐Herráez

Katsigiannis

et al. 2021

IEEE Trans. Affective Comput.

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Biometric signals have been extensively used for user identification and authentication due to their inherent characteristics that are unique to each person. The variation exhibited between the brain signals (EEG) of different people makes such signals especially suitable for biometric user identification. However, the characteristics of these signals are also influenced by the user's current condition, including his/her affective state. In this paper, we analyze the significance of the affect-related component of brain signals within the subject identification context. Consistent results are obtained across three different public datasets, suggesting that the dominant component of the signal is subject-related, but the affective state also has a contribution that affects identification accuracy. Results show that identification accuracy increases when the system has been trained with EEG recordings that refer to similar affective states as the sample that is to be identified. This improvement holds independently of the features and classification algorithm used, and it is generally above 10% under a rigorous setting, when the training and validation datasets do not share data from the same recording days. This finding emphasizes the potential benefits of considering affective information in applications that require subject identification, such as user authentication.

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“…Actually, several researches re cently confirmed the discriminative capability of EEG data [7]. Moreover, the rapid development of technology makes plausible EEG-based recognition systems for real-life appli cations in the near future [8].…”

Section: Eeg Biometricsmentioning

confidence: 98%

EEG-based biometric recognition using EigenBrains

Maiorana

Rocca

Campisi

2015

2015 IEEE International Conference on Multimedia &Amp; Expo Workshops (ICMEW)

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An increased level of attention has recently raised on biometric recognition by means of electroencephalography (EEG). This modality in fact possesses several properties which may be appealing for automatic people recognition, such as the intrinsic liveness detection and the robustness against potential attacks. Moreover, it could be easily exploited in applications based on brain-computer interfaces (BCI). In this paper we exhaustively analyze the discriminative capability of a compact representation of EEG signals acquired in resting conditions. Specifically, the exploited templates are obtained as projections into a subspace defined through EigenBrains (EBs), a basis for EEG data relying on principal component analysis (PCA). An extensive set of experimental tests, conducted on a database comprising 60 users, is performed to evaluate the recognition capabilities of the proposed representation under different system configurations

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Electroencephalogram subject identification: A review

Cited by 109 publications

References 72 publications

Variability and stability of large-scale cortical oscillation patterns

Variability and stability of large-scale cortical oscillation patterns

On the Influence of Affect in EEG-Based Subject Identification

EEG-based biometric recognition using EigenBrains

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