Personal Identity Verification by EEG-Based Network Representation on a Portable Device

Orrù, Giulia; Garau, Marco; Fraschini, Matteo; Acedo, Javier; Didaci, Luca; Ibañez, David P.; Soria-Frish, Aureli; Marcialis, Gian Luca

doi:10.1007/978-3-030-29891-3_15

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…In a recent study for EEG biometrics, Goudiaby et al [56] performed multi-channel verification using the Emotiv Epoc system, and Zeynali and Seyedarabi [57] classified single channel signal recordings using ANN, Support Vector Machine (SVM) and Bayes classifier. Orrú et al [58] showed that an effective EEG-based verification can be made by using a smaller number of sensors, and they confirmed that the gamma sub-band contains the best distinguishing data in person verification.…”

Section: Introductionmentioning

confidence: 99%

High-Dimension EEG Biometric Authentication Leveraging Sub-Band Cube-Code Representation

Esener,

Kılınç,

Urazel

et al. 2023

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Advancements in EEG biometric technologies have been hindered by two persistent challenges: the management of large data sizes and the unreliability of data resulting from various measurement environments. Addressing these challenges, this study introduces a novel methodology termed 'Cube-Code' for cognitive biometric authentication. As a preliminary step, Automatic Artifact Removal (AAR) leveraging wavelet Independent Component Analysis (wICA) is applied to EEG signals. This step transforms the signals into independent sub-components, effectively eliminating the effects of muscle movements and eye blinking. Subsequently, unique 3-Dimensional (3-D) Cube-Codes are generated, each representing an individual subject in the database. Each Cube-Code is constructed by stacking the alpha, beta, and theta sub-band partitions, obtained from each channel during each task, back-to-back. This forms a third-order tensor. The stacking of these three subbands within a Cube-Code not only prevents a dimension increase through concatenation but also permits the direct utilization of non-stationary data, bypassing the need for fiducial component detection. Higher-Order Singular Value Decomposition (HOSVD) is then applied to perform a subspace analysis on each Cube-Code, an approach supported by previous literature concerning its effectiveness on 3-D tensors. Upon completion of the decomposition process, a flattening operation is executed to extract lower-dimensional, taskindependent feature matrices for each subject. These feature matrices are then employed in five distinct deep learning architectures. The Cube-Code methodology was tested on EEG signals, composed of different tasks, from the PhysioNet EEG Motor Movement/Imagery (EEGMMI) dataset. The results demonstrate an authentication accuracy rate of approximately 98%. In conclusion, the novel Cube-Code methodology provides highly accurate results for subject recognition, delivering a new level of reliability in EEG-based biometric authentication.

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Section: Introductionmentioning

confidence: 99%

High-Dimension EEG Biometric Authentication Leveraging Sub-Band Cube-Code Representation

Esener,

Kılınç,

Urazel

et al. 2023

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“…On the basis of our previous works, which showed that the fusion of multiple matchers can help even in EEG-based personal recognition based on functional connectivity measurements [11,12] and the sharp potentials of deep learning shown in [2,7], we propose an EEG-based personal identification system based on the fusion of the classification over multiple patches. By this term, we mean very short portions of the electroencephalographic trace.…”

Section: Introductionmentioning

confidence: 99%

EEG personal recognition based on ‘qualified majority’ over signal patches

et al. 2021

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Electroencephalography (EEG)-based personal recognition in realistic contexts is still a matter of research, with the following issues to be clarified: (1) the duration of the signal length, called 'epoch', which must be very short for practical purposes and (2) the contribution of EEG sub-bands. These two aspects are connected because the shorter the epoch's duration, the lower the contribution of the low-frequency sub-bands while enhancing the high-frequency sub-bands. However, it is well known that the former characterises the inner brain activity in resting or unconscious states. These sub-bands could be of no use in the wild, where the subject is conscious and not in the condition to put himself in a resting-state-like condition. Furthermore, the latter may concur much better in the process, characterising normal subject activity when awake. This study aims at clarifying the problems mentioned above by proposing a novel personal recognition architecture based on extremely short signal fragments called 'patches', subdividing each epoch. Patches are individually classified. A 'qualified majority' of classified patches allows taking the final decision. It is shown by experiments that this approach (1) can be adopted for practical purposes and (2) clarifies the sub-bands' role in contexts still implemented in vitro but very similar to that conceivable in the wild.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Personal Identity Verification by EEG-Based Network Representation on a Portable Device

Cited by 2 publications

References 7 publications

High-Dimension EEG Biometric Authentication Leveraging Sub-Band Cube-Code Representation

High-Dimension EEG Biometric Authentication Leveraging Sub-Band Cube-Code Representation

EEG personal recognition based on ‘qualified majority’ over signal patches

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