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EEG-Based Person Identification Using Multi-Verse Optimizer as Unsupervised Clustering Techniques

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Evolutionary Data Clustering: Algorithms and Applications

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Recently, electroencephalogram (EEG) signal provides great potential for identification systems. Many studies have shown that the EEG introduces unique, universal features and natural robustness for spoofing attacks. The EEG represents a graphic recording of the electrical activity of the brain that can be measured by placing sensors (electrodes) at different locations on the scalp. This chapter proposes a new technique using unsupervised clustering and optimization techniques for user identification-based EEG signals. The proposed method employs four algorithms which are Genetic Algorithm (GA), Multi-verse Optimizer (MVO), Particle Swarm Optimization (PSO) and the k-means algorithm. A standard EEG motor imagery dataset is used to evaluate the proposed method’s performance, and its results are evaluated using four criteria: (i) Precision, (ii) Recall, (iii) F-Score and (v) Purity. It is worth mentioning that this work is one of the first to employ optimization methods with unsupervised clustering methods for person identification using EEG. As a conclusion, the MVO algorithm achieved the best results compared with GA, PSO and k-means. Finally, the proposed method can draw future directions to apply to different research areas.

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Notes

  1. 1.

    Such numbers are suggested in the work of Rodrigues et al. [9, 50].

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Author information

Authors and Affiliations

  1. ECE Department, Faculty of Engineering, University of Kufa, Najaf, Iraq

    Zaid Abdi Alkareem Alyasseri

  2. Faculty of Information Science and Technology, Center for Artificial Intelligence, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Zaid Abdi Alkareem Alyasseri & Salwani Abdullah

  3. School of Computer Sciences, Universiti Sains Malaysia, George Town, Pulau Pinang, Malaysia

    Ammar Kamal Abasi, Sharif Naser Makhadmeh & Ahamad Tajudin Khader

  4. Department of Information Technology - MSAI, College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates

    Mohammed Azmi Al-Betar

  5. IT Department, Al-Huson University College, Al-Balqa Applied University, Irbid, Jordan

    Mohammed Azmi Al-Betar

  6. Department of Computing, São Paulo State University - UNESP, Bauru, Brazil

    João P. Papa

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  1. Zaid Abdi Alkareem Alyasseri
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  2. Ammar Kamal Abasi
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  3. Mohammed Azmi Al-Betar
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  4. Sharif Naser Makhadmeh
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  7. Ahamad Tajudin Khader
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Corresponding author

Correspondence to Zaid Abdi Alkareem Alyasseri .

Editor information

Editors and Affiliations

  1. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Ibrahim Aljarah

  2. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Hossam Faris

  3. Center for Artificial Intelligence Research and Optimization, Torrens University Australia, Brisbane, QLD, Australia

    Seyedali Mirjalili

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Alyasseri, Z.A.A. et al. (2021). EEG-Based Person Identification Using Multi-Verse Optimizer as Unsupervised Clustering Techniques. In: Aljarah, I., Faris, H., Mirjalili, S. (eds) Evolutionary Data Clustering: Algorithms and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4191-3_4

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