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SVM and Ensemble-SVM in EEG-Based Person Identification

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Progress in Advanced Computing and Intelligent Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1199))

Abstract

Biometric person identification is getting more effective and popular because of Electroencephalography (EEG). EEG signals can be captured from human scalp invasively or non-invasively with the help of electrodes. EEG-based biometric system is more secure and unique for person identification. In this paper, we have used two different states to explore the adaptive and uniqueness of the EEG-based biometric system. We have used Eyes Open (EO) state as well as Eyes Closed (EC) state of a EEG motor imagery publicly available dataset of 109 users.The model is trained and tested with EO and EC states alternatively to prove the reliability and robustness of the model. The biometric person identification model has been designed using Support Vector machine (SVM) for classification. We achieved a notable person identification rate of 96% (EO) and 91.78% (EC) using SVM with Radial Basis Function (RBF) kernel. We have also used Ensemble Support Vector Machine (ESVM) to enhance the performance of person identification and observed the average performance accuracy of 96.16% with n number of classifier.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, NIT, Rourkela, India

    Banee Bandana Das, Korra Sathya Babu & Ramesh Kumar Mohapatra

  2. Department of Electronics and Communication Engineering, NIT, Rourkela, India

    Saswat Kumar Ram

  3. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, India

    Bibudhendu Pati & Chhabi Rani Panigrahi

Authors
  1. Banee Bandana Das
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  2. Saswat Kumar Ram
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  3. Bibudhendu Pati
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  4. Chhabi Rani Panigrahi
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  5. Korra Sathya Babu
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  6. Ramesh Kumar Mohapatra
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Corresponding author

Correspondence to Banee Bandana Das .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Rama Devi Women's University, Bhubaneswar, India

    Chhabi Rani Panigrahi

  2. Department of Computer Science, Rama Devi Women's University, Bhubaneswar, India

    Bibudhendu Pati

  3. Department of Computer Science, University of California, Davis, CA, USA

    Prasant Mohapatra

  4. Cloud Computing and Distributed Systems (CLOUDS) Lab, School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

  5. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

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Das, B.B., Ram, S.K., Pati, B., Panigrahi, C.R., Babu, K.S., Mohapatra, R.K. (2021). SVM and Ensemble-SVM in EEG-Based Person Identification. In: Panigrahi, C.R., Pati, B., Mohapatra, P., Buyya, R., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1199. Springer, Singapore. https://doi.org/10.1007/978-981-15-6353-9_13

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  • DOI: https://doi.org/10.1007/978-981-15-6353-9_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-6352-2

  • Online ISBN: 978-981-15-6353-9

  • eBook Packages: EngineeringEngineering (R0)

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