Daria La Rocca

Brain waves for automatic biometric-based user recognition

Brain signals have been investigated within the medical field for more than a century to study brain diseases like epilepsy, spinal cord injuries, Alzheimers, Parkinsons, schizophrenia, and stroke among others. They are also used in both brain computer and brain machine interface systems with assistance, rehabilitative, and entertainment applications. Despite the broad interest in clinical applications, the use of brain signals has been only recently investigated by the scientific community as a biometric characteristic to be used in automatic people recognition systems. However, brain signals present some peculiarities, not shared by the most commonly used biometrics, such as face, iris, and fingerprints, with reference to privacy compliance, robustness against spoofing attacks, possibility to perform continuous identification, intrinsic liveness detection, and universality. These peculiarities make the use of brain signals appealing. On the other hand, there are many challenges which need to be properly addressed. The understanding of the level of uniqueness and permanence of brain responses, the design of elicitation protocols, and the invasiveness of the acquisition process are only few of the challenges which need to be tackled. In this paper, we further speculate on those issues, which represent an obstacle toward the deployment of biometric systems based on the analysis of brain activity in real life applications and intend to provide a critical and comprehensive review of state-of-the-art methods for electroencephalogram-based automatic user recognition, also reporting neurophysiological evidences related to the performed claims.

Evaluation of the brain network organization from EEG signals: a preliminary evidence in stroke patient.

Synchronous brain activity in motor cortex in perception or in complex cognitive processing has been the subject of several studies. The advanced analysis of cerebral electro‐physiological activity during the course of planning (PRE) or execution of movement (EXE) in a high temporal resolution could reveal interesting information about the brain functional organization in patients following stroke damage. High‐power (128 channels) electroencephalography registration was carried out on 8 healthy subjects and on a patient with stroke with capsular lacuna in the right hemisphere. For activation of motor cortex, the finger tapping paradigm was used. In this preliminary study, we tested a theoretical graph approach to characterize the task‐related spectral coherence. All of the obtained brain functional networks were analyzed by the connectivity degree, the degree distribution, and efficiency parameters in the Theta, Alpha, Beta, and Gamma bands during the PRE and EXE intervals. All the brain networks were found to hold a regular and ordered topology. However, significant differences (P < 0.01) emerged between the patient with stroke and the control subjects, independently of the neural processes related to the PRE or EXE periods. In the Beta (13–29 Hz) and Gamma (30–40 Hz) bands, the significant (P < 0.01) decrease in global‐ and local‐efficiency in the patients networks, reflected a lower capacity to integrate communication between distant brain regions and a lower tendency to be modular. This weak organization is principally due to the significant (P < 0.01 Bonferroni corrected) increase in disconnected nodes together with the significant increase in the links in some other crucial vertices. Anat Rec, 292:2023–2031, 2009.

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, security and robustness, 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 subjects acquired in two different sessions, separated by one week time.

Eigenbrains and Eigentensorbrains

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. HighlightsTwo novel parsimonious EEG representations proposed for biometric recognition.Performance evaluated on EEG data taken in two distinct sessions from 30 subjects.Analysis of the discriminability of distinct EEG sub-bands, with several classifiers.LDA improves the performance if applied to either ETB or EB representations.For recognition, ETB projections better than EB, EC resting conditions better than EO.

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 vulnerability of an EEG-based biometric system to hill-climbing attacks algorithms' comparison and possible countermeasures

In this paper we analyze the vulnerability to hill-climbing attacks of a biometric recognition system based on electroencephalography (EEG). It is assumed that an attacker can access the scores produced by the employed matcher, and use them to control the generation of synthetic EEG templates until achieving a successful authentication. To this aim, different general approaches relying on function optimization are evaluated and compared in terms of authentication success rate and average number of required attempts. The possibility of increasing the system robustness against this kind of attacks, without significantly affecting its recognition performance, is also investigated.

Stable EEG Features for Biometric Recognition in Resting State Conditions

In this paper electroencephalogram (EEG) signals are studied to extract biometric traits for identification of users. Different recording sessions separated in time are considered in order to infer about usability of EEG biometrics in real life applications. The aim of this work is to provide a representation of the data and a classification approach which would show repeatability of the EEG features employed in the proposed framework. The brain electrical activity has already shown some potentials to allow automatic user recognition, but an extensive analysis of EEG data aiming at retain stable and distinctive features is still missing. In this contribution we test the invariance over time of the discriminant power of the employed EEG features, which is a relevant property for a biometric identifier to be employed in real life applications. The enrolled healthy subjects performed resting state recordings on two different days. Combinations of different electrodes and spectral subbands have been analyzed to infer about the distinctiveness of different topographic traits and oscillatory activities. Autoregressive statistical modeling using reflection coefficients has been adopted and a linear classifier has been tested. The observed results show that a high degree of accuracy can be achieved considering different acquisition sessions for the enrollment and the testing stage. Moreover, a proper information fusion at the match score level showed to improve performance while reducing the sample size used for the testing stage.

Brain response to Information Structure misalignments in linguistic contexts

Abstract The paper inquires, through the analysis of electroencephalographic (EEG) recordings, the processing costs associated to misalignments between the information status (Given/New) of discourse contents and their linguistic packaging as Topic or Focus in discourse. The way information is packaged within utterances, that is, their Information Structure, guides language comprehension. Sentences are typically organized into Topic and Focus units, commonly conveying Given (already active in working memory) and New (not active) information, respectively. Nonetheless, for precise purposes, novel information can be presented in Topic, and known information in Focus. The paper accounts for the efficiency of brain processing in response to such “violations” of Information Structure, through both EEG spectral analysis and whole-brain functional connectivity patterns. The main contribution of the present work is the analysis of brain responses in natural contexts, i.e. when processing whole texts of more sentences, instead of isolated (couples of) utterances as is the case of a number of experimental paradigms pursued in the psycholinguistic domain. EEG signals recorded from a population of 54 subjects highlight the presence of rhythmic changes in different frequency bands, depending on aligned and misaligned Information Structures.

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.