Improving the Feature Stability and Classification Performance of Bimodal Brain and Heart Biometrics

Palaniappan, Ramaswamy and Andrews, Samraj and Sillitoe, Ian and Sihra, Tarsem and Paramesran, Raveendran (2015) Improving the Feature Stability and Classification Performance of Bimodal Brain and Heart Biometrics. In: Advances in Signal Processing and Intelligent Recognition Systems. Springer, pp. 176-185. ISBN 21945357. (doi:10.1007/978-3-319-28658-7_15)

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Abstract

Electrical activities from brain (electroencephalogram, EEG) and heart (electrocardiogram, ECG) have been proposed as biometric modalities but the combined use of these signals appear not to have been studied thoroughly. Also, the feature stability of these signals has been a limiting factor for biometric usage. This paper presents results from a pilot study that reveal the combined use of brain and heart modalities provide improved classification performance and further-more, an improvement in the stability of the features over time through the use of binaural brain entrainment. The classification rate was increased, for the case of the neural network classifier from 92.4% to 95.1% and for the case of LDA, from 98.6% to 99.8%. The average standard deviation with binaural brain entrainment using all the inter-session features (from all the subjects) was 1.09, as compared to 1.26 without entrainment. This result suggests the improved stability of both the EEG and ECG features over time and hence resulting in higher classification performance. Overall, the results indicate that combining ECG and EEG gives improved classification performance and that through the use of binaural brain entrainment, both the ECG and EEG features are more stable over time.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-319-28658-7_15
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.9.H85 Human computer interaction
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Faculties > University wide - Teaching/Research Groups > Centre for Cognitive Neuroscience and Cognitive Systems
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Mar 2016 12:28 UTC
Last Modified: 29 May 2019 17:06 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/54539 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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