A biometric-based covert warning system using EEG

Su, Fei; Zhou, Huangling; Zhi-yin, Feng; Ma, Junshui

doi:10.1109/icb.2012.6199830

Cited by 29 publications

(15 citation statements)

References 9 publications

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“…This makes such signals difficult to synthesize and therefore improves the resistance of biometric systems to spoofing attacks. Besides electroencephalogram (EEG), as a biometric modality, could be used to send covert warning when the authorized user is under external forcing conditions, as implemented in [87].…”

Section: Security and Authenticationmentioning

confidence: 99%

Brain computer interfacing: Applications and challenges

Abdulkader

Atia

Mostafa

2015

Egyptian Informatics Journal

427

247

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Brain computer interface technology represents a highly growing field of research with application systems. Its contributions in medical fields range from prevention to neuronal rehabilitation for serious injuries. Mind reading and remote communication have their unique fingerprint in numerous fields such as educational, self-regulation, production, marketing, security as well as games and entertainment. It creates a mutual understanding between users and the surrounding systems. This paper shows the application areas that could benefit from brain waves in facilitating or achieving their goals. We also discuss major usability and technical challenges that face brain signals utilization in various components of BCI system. Different solutions that aim to limit and decrease their effects have also been reviewed.Ó 2015 Production and hosting by Elsevier B.V. on behalf

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Section: Security and Authenticationmentioning

confidence: 99%

Brain computer interfacing: Applications and challenges

Abdulkader

Atia

Mostafa

2015

Egyptian Informatics Journal

427

247

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“…They reflect the occurrence of motor or mental activities in various Brain Computer Interface (BCI) applications. The brain responses to certain actions have been used to verify the claimed identity even for people with various disabilities or to convey secret messages through the identification process as implied by Su et al in [3]. They have designed an identification system with the ability to detect a covert warning expressed as a clenching-teeth muscle activity.…”

Section: Related Workmentioning

confidence: 98%

Single Trial Authentication with Mental Password Writing

Abdulkader

Atia

Mostafa

2015

Lecture Notes in Computer Science

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Abstract. This paper presents an authentication system that uses brain waves as a biometric discriminant trait. It utilizes Electroencephalogram (EEG) signals generated from mental writing of the user-owned password. Independent Component Analysis (ICA) and baseline correction has been used for preprocessing and noise removal. The effect of two types of features, multivariate autoregressive (MVAR) model parameters and power spectral density (PSD) features, have been studied for this activity. Performance results based on single trial analysis have revealed that imagined password writing can reach average Half Total Error Rate (HTER) of 5 % for PSD features vs 3 % obtained with MVAR coefficients. The experiments have shown that mental password writing can be used for increasing the user acceptance for enrollment conditions while maintaining high performance results.

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“…Visual and audi tory events can be for instance employed for provoking event related potentials (ERPs), characterized by a precise tempo ral reference for the analysis of specific EEG segments [9]. The execution of real or imagined tasks such as body move ments, mental calculation or speech may be also considered [10]. Nonetheless, the most widely employed acquisition pro tocol consists in the recording of brain activity during a rest ing state, where subjects avoid any focusing or concentration while remaining awake and alert.…”

Section: Eeg Biometricsmentioning

confidence: 99%

“…The power spectral density (PSD) of EEG signals has been in fact already employed for extracting biometric features as in [11], where the ex rhythm is evaluated for recognition pur poses. The PSD in the [5 --;.-32] Hz range of a single-channel EEG is also employed in [10] for designing a covert warning system working in resting conditions. In this paper we ex ploit PCA to define an orthogonal basis for representing EEG spectral information, in such a way that most of its original variance is retained within a limited set of coefficients.…”

Section: Eeg Templates For User Recognition: Eigenbrain Projectionsmentioning

confidence: 99%

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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A biometric-based covert warning system using EEG

Cited by 29 publications

References 9 publications

Brain computer interfacing: Applications and challenges

Brain computer interfacing: Applications and challenges

Single Trial Authentication with Mental Password Writing

EEG-based biometric recognition using EigenBrains

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