A pervasive EEG-based biometric system

Hu, B. and Mao, C. and Campbell, W.M. and Moore, P. and Liu, L. and Zhao, G. (2011) A pervasive EEG-based biometric system. In: 2011 ACM Workshop on Ubiquitous Affective Awareness and Intelligent Interaction, UAAII'11, Co-located with UbiComp 2011, 18 September 2011 through 18 September 2011, Beijing; China.

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Abstract

Identification of individuals is ubiquitous with increasing reliance by financial and governmental organizations on reliable and robust personal recognition systems to determine and confirm the identity and policy constraints for specific individuals in 'real-time' when reacting to service requests. The traditional identification approaches to user validation are not robust and deficiencies in such approaches are becoming increasingly apparent in the current information-oriented society. With developments in research into the human brain, biometric methods based on brain wave signals have received increased attention as an effective approach to user validation as an individuals' brain wave signals cannot be duplicated, discarded or stolen. Targeted at pervasive systems and the identified deficiencies in traditional approaches to user identification and validation, an electroencephalogram (EEG)-based biometric system for use in pervasive environments is proposed in this paper. A significant problem of EEG-based biometrics in pervasive environment is the requirement of real-time and convenience. In our study, only one active electrode with a portable EEG collection device was used and no other instructions to users for convenience; in addition, the signal analysis methods we used were efficient to achieve less time consumption. In our prototype system, 11 subjects were identified with recognition accuracy in the range 66.02% to 100%; the recognition accuracy increased with increases in the EEG sample time; and the computational time of signal analysis was about 0.5s. The low computational time of EEG data analysis validates this model when implemented in pervasive environments. For differing applications we can define a suitable balance point to optimize the conflicting demands of data collection time length and recognition accuracy. © 2011 ACM.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: biometrics, eeg, individual identification, pervasive system, Active electrodes, Analysis method, Balance point, Biometric methods, Biometric systems, Brain wave, Collection device, Computational time, Data collection, Human brain, Identification approach, Identification of individuals, Individual identification, Information-oriented society, Personal recognition, Pervasive environments, Pervasive systems, Policy constraints, Prototype system, Recognition accuracy, Service requests, Time consumption, User identification, Biometrics, Data reduction, Face recognition, Real time systems, Routers, Signal analysis, Electroencephalography
Subjects: G400 Computer Science
Divisions: UoA Collections > UoA11: Computer Science and Informatics
Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Hussen Farooq
Date Deposited: 20 Jul 2016 10:04
Last Modified: 20 Jul 2016 10:04
URI: http://www.open-access.bcu.ac.uk/id/eprint/2075

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