EEG-based biometric recognition using EigenBrains

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

Eigenbrains and Eigentensorbrains: Parsimonious bases for EEG biometrics

The use of electroencephalography (EEG) for biometric recognition purposes has recently received an increased level of attention thanks to some of its appealing properties. Among them, it is worth mentioning the universality, the intrinsic liveness detection capability, the possibility to perform a continuous identification, and the robustness against spoofing attacks. In this paper we exhaustively analyze the recognition performance achievable when using a parsimonious representation, in the frequency domain, of EEG signals acquired in both eyes-closed (EC) and eyes-open (EO) resting conditions. Specifically, we evaluate the effectiveness of EEG templates obtained as projections onto subspaces defined through eigenbrains (EBs) or eigentensorbrains (ETBs), two bases for EEG signals here defined by means of principal component analysis (PCA) and multilinear PCA (MPCA). An extensive set of experimental tests, conducted on a database comprising EEG recordings acquired from 30 subjects during two separate sessions, in different days, is performed to compare the recognition capabilities of the considered representations under different system configurations

Cognitive biometric cryptosystems a case study on EEG

In this paper we propose several cryptosystems for the protection of templates extracted from cognitive biometrics. The proposed architectures exploit different possibilities for combining the information made continuously available during the recognition phase. Specifically, we focus on electroencephalog-raphy (EEG) signals as considered biometrics. Brain waves are in fact one of the most emerging cognitive modalities to be used for people recognition. An extensive set of experimental tests, performed on a large database comprising recordings taken from 40 healthy subjects during two separate recording sessions, is carried out to evaluate the recognition rates and security levels achievable with several system configurations

On the Permanence of EEG Signals for Biometric Recognition

Brain signals have been investigated for more than a century in the medical field. However, despite the broad interest in clinical applications, their use as a biometric identifier has been only recently considered by the scientific community. In this paper, we focus on the permanence across time of brain signals, specifically of electroencephalographic (EEG) signals, issue of paramount importance for the deployment of brain-based biometric recognition systems in real life, not yet fully addressed. In particular, we speculate about the stability of EEG features by analyzing the recognition performance that can be achieved when comparing EEG signals acquired during different sessions. We carry out an extensive set of experimental tests, performed on several EEG-based biometric systems over a large database, comprising three recordings taken from 50 healthy subjects in resting state conditions, acquired in a time span of approximately one month and a half. The results confirm that a significant level of permanence can be guaranteed

EEG biometrics for user recognition using visually evoked potentials

Electroencephalographic signals (EEG) have been long supposed to contain features characteristic of each individual, yet a substantial interest for exploiting them as a potential biometrics for people recognition has only recently grown. The biggest advantages of EEG-based biometrics lie in its universality and security, while its major concerns are related to the acquisition protocol that can be inconvenient and time consuming. This paper investigates the use of EEG signals, elicited using visual stimuli, for the purpose of biometric recognition, and evaluates the performance obtained considering various frequency bands, different number of visual stimuli, and various subsets of time intervals after the stimuli presentation. An exhaustive set of experimental tests has been performed by employing EEG data of 50 different healthy subjects acquired in two different sessions, separated by one week time

Emergence of β and γ networks following multisensory training

Our perceptual reality relies on inferences about the causal structure of the world given by multiple sensory inputs. In ecological settings, multisensory events that cohere in time and space benefit inferential processes: hearing and seeing a speaker enhances speech comprehension, and the acoustic changes of flapping wings naturally pace the motion of a flock of birds. Here, we asked how a few minutes of (multi)sensory training could shape cortical interactions in a subsequent perceptual task, and investigated oscillatory activity and functional connectivity as a function of sensory history in training. Human participants performed a visual motion coherence discrimination task while being recorded with magnetoencephalography (MEG). Three groups of participants performed the same task with visual stimuli only, while listening to acoustic textures temporally comodulated with the strength of visual motion coherence, or with auditory noise uncorrelated with visual motion. The functional connectivity patterns before and after training were contrasted to resting-state networks to assess the variability of common task-relevant networks, and the emergence of new functional interactions following training. One main finding is the emergence of a large-scale synchronization in the high γ (gamma: 60 –– 120Hz) and β (beta:15 — 30Hz) bands for individuals who underwent comodulated multisensory training. The post-training network involved prefrontal, parietal, and visual cortices. Our results suggest that the integration of evidence and decision-making strategies become more efficient following congruent multisensory training through plasticity in network routing and oscillatory regimes

Multifractal analysis for cumulant-based epileptic seizure detection in eeg time series

International audienceMultifractal analysis allows us to study scale invariance and fluctuations of the pointwise regularity of time series. A theoretically well grounded multifractal formalism, based on wavelet leaders, was applied to electroencephalogra-phy (EEG) time series measured in healthy volunteers and epilepsy patients, provided by the University of Bonn. We show that the multifractal spectrum during a seizure indicates a lower global regularity when compared to non-seizure data and that multifractal features, combined with few baseline features, can be used to train a supervised learning algorithm to discriminate well above chance ictal (i.e. seizure) versus healthy and interictal epochs (97 %) and healthy controls versus patients (92 %)

Revisiting Functional Connectivity for Infraslow Scale-Free Brain Dynamics using Complex Wavelet

International audienceThe analysis of human brain functional networks is achieved by computing functional connectivity indices reflecting phase coupling and interactions between remote brain regions. In magneto-and electroencephalography, the most often used functional connectivity indices are constructed on Fourier-based cross spectral estimation applied to specific fast and band limited oscillatory regimes. Recently, infraslow arrhythmic fluctuations (below the 1Hz) were recognized as playing a leading role in spontaneous brain activity. The present work aims to propose to assess functional connectivity, from fractal dynamics, thus extending the assessment of functional connectivity to the infraslow arrhythmic or scale-free temporal dynamics of M/EEG-quantified brain activity. Instead of being based on Fourier analysis, new Imaginary Coherence and weighted Phase Lag indices are constructed from complex-wavelet representations. Their performance are, first, assessed on synthetic data, by means of Monte-Carlo simulations, and compared favorably against the classical Fourier-based indices. These new assessment of functional connectivity indices are, second, applied to MEG data collected on 36 individuals, both at rest and during the learning of a visual motion discrimination 1 La Rocca et al. Functional Connectivity assessed from fractal dynamics task. They demonstrate a higher statistical sensitivity, compared to their Fourier counterparts, in capturing significant and relevant functional interactions in the infraslow regime, and modulations from rest to task. Notably, the consistent overall increase in functional connectivity assessed from fractal dynamics from rest to task, correlated with a change in temporal dynamics, as well as with improved performance in task completion, suggests that complex-wavelet weighted Phase Lag index is the sole index able to capture brain plasticity in the infraslow scale-free regime