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Electroencephalogram signals from imagined activities: A novel biometric identifier for a small population

Palaniappan, Ramaswamy (2006) Electroencephalogram signals from imagined activities: A novel biometric identifier for a small population. In: Intelligent Data Engineering and Automated Learning – IDEAL 2006 7th International Conference. Lecture Notes in Computer Science . Springer, Berlin, Germany, pp. 604-611. ISBN 978-3-540-45485-4. E-ISBN 978-3-540-45487-8. (doi:10.1007/11875581_73) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1007/11875581_73

Abstract

Electroencephalogram (EEG) signals extracted during imagined activities have been studied for use in Brain Computer Interface (BCI) applications. The major hurdle in the EEG based BCI is that the EEG signals are unique to each individual. This complicates a universal BCI design. On the contrary, this disadvantage is the advantage when it comes to using EEG signals from imagined activities for biometric applications. Therefore, in this paper, EEG signals from imagined activities are proposed as a biometric to identify the individuality of persons. The approach is based on the classification of EEG signals recorded when a user performs either one or several mental activities (up to five). As different individuals have different thought processes, this idea would be appropriate for individual identification. To increase the inter-subject differences, EEG data from six electrodes are used instead of one. A total of 108 features (autoregressive coefficients, channel spectral powers, inter-hemispheric channel spectral power differences and inter-hemispheric channel linear complexity values) are computed from each EEG segment for each mental activity and classified by a linear discriminant classifier using a modified 10 fold cross validation procedure, which gave perfect classification when tested on 500 EEG patterns from five subjects. This initial study has shown the huge potential of the method over existing biometric identification systems as it is impossible to be faked.

Item Type: Book section
DOI/Identification number: 10.1007/11875581_73
Additional information: Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - Lect. Notes Comput. Sci. [Field not mapped to EPrints] AD - Dept. of Computer Science, University of Essex, Colchester, United Kingdom [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Conference Paper [Field not mapped to EPrints] C3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [Field not mapped to EPrints]
Uncontrolled keywords: Biometrics, Brain computer interface, Electroencephalogram, Imagined activities, Brain, Channel capacity, Classification (of information), Data acquisition, Electrodes, Feature extraction, Human computer interaction, Image reconstruction, Interfaces (computer), Biometric applications, Biometric identification systems, Brain computer interfaces, Imagined activities, Electroencephalography
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 14:49 UTC
Last Modified: 29 May 2019 13:59 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70734 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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