IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i19p3442-d921508.html
   My bibliography  Save this article

Convolutional Neural Network for Closed-Set Identification from Resting State Electroencephalography

Author

Listed:
  • Chi Qin Lai

    (Intel PG16, Bayan Lepas Technoplex Medan Bayan Lepas, Bayan Lepas 11900, Penang, Malaysia)

  • Haidi Ibrahim

    (School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Penang, Malaysia)

  • Shahrel Azmin Suandi

    (School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Penang, Malaysia)

  • Mohd Zaid Abdullah

    (School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Penang, Malaysia)

Abstract

In line with current developments, biometrics is becoming an important technology that enables safer identification of individuals and more secure access to sensitive information and assets. Researchers have recently started exploring electroencephalography (EEG) as a biometric modality thanks to the uniqueness of EEG signals. A new architecture for a convolutional neural network (CNN) that uses EEG signals is suggested in this paper for biometric identification. A CNN does not need complex signal pre-processing, feature extraction, and feature selection stages. The EEG datasets utilized in this research are the resting state eyes open (REO) and the resting state eyes closed (REC) EEG. Extensive experiments were performed to design this deep CNN architecture. These experiments showed that a CNN architecture with eleven layers (eight convolutional layers, one average pooling layer, and two fully connected layers) with an Adam optimizer resulted in the highest accuracy. The CNN architecture proposed here was compared to existing models for biometrics using the same dataset. The results show that the proposed method outperforms the other task-free paradigm CNN biometric identification models, with an identification accuracy of 98.54%.

Suggested Citation

  • Chi Qin Lai & Haidi Ibrahim & Shahrel Azmin Suandi & Mohd Zaid Abdullah, 2022. "Convolutional Neural Network for Closed-Set Identification from Resting State Electroencephalography," Mathematics, MDPI, vol. 10(19), pages 1-16, September.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3442-:d:921508
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/19/3442/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/19/3442/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Boaretto, Bruno R.R. & Budzinski, Roberto C. & Rossi, Kalel L. & Masoller, Cristina & Macau, Elbert E.N., 2023. "Spatial permutation entropy distinguishes resting brain states," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3442-:d:921508. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.