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Brain Signal Based Biometric Identification Using One-Dimensional Local Gradient Pattern and Artificial Neural Network

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Computational Intelligence, Communications, and Business Analytics (CICBA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 775))

Abstract

Biometric identification or recognition refers to the process of identifying an unknown individual based on the physiological or behavioral characteristics. While fingerprint, palm, face belongs to physiological characteristics, traits like voice, gait falls in the category of behavioral characteristics. Recently, there has been an increase in interest in develo** neural signal based biometric identification system as the brain signals have certain unique features related to an individual and they are difficult to mimic as well. Electroencephalogram (EEG) captures the brain electrical activity and used in different applications including health care and human-computer interaction. In this paper, a new approach with the combination of one-dimensional Local Gradient Pattern (1D-LGP) and Artificial Neural Network (ANN) has been introduced for building EEG signal based biometric identification system. The proposed framework consists of two steps. In the first step, the 1D-LGP code is computed for each signal point in the EEG signal and the histogram is formed. The histogram represents the extracted feature vector of the corresponding EEG signal which is then fed to the ANN classifier to perform the classification. The experiment has been carried-out with the benchmark dataset having 20 subjects. The system performance has been evaluated using the mean accuracy obtained after 20 runs of 10-fold cross validation. The experimental results show that the proposed technique achieved a high accuracy.

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Notes

  1. 1.

    EEG Dataset https://archive.ics.uci.edu/ml/datasets/EEG+Database.

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Acknowledgments

The authors would like to thank Henri Begleiter, Neurodynamics Laboratory, State University of New York Health Center, Brooklyn, for the dataset used in this research.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (ISM) Dhanbad, Dhanbad, 826004, Jharkhand, India

    Abeg Kumar Jaiswal & Haider Banka

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  1. Abeg Kumar Jaiswal
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  2. Haider Banka
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Correspondence to Abeg Kumar Jaiswal .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

  2. Department of Computer and System Sciences, Visva Bharati University, Bolpur Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Information Technology, Calcutta Business School, Kolkata, India

    Somnath Mukhopadhyay

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Jaiswal, A.K., Banka, H. (2017). Brain Signal Based Biometric Identification Using One-Dimensional Local Gradient Pattern and Artificial Neural Network. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 775. Springer, Singapore. https://doi.org/10.1007/978-981-10-6427-2_42

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  • DOI: https://doi.org/10.1007/978-981-10-6427-2_42

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  • Online ISBN: 978-981-10-6427-2

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