Kaewwit, Chesada and Lursinsap, Chidchanok and Sophatsathit, Peraphon (2017) High Accuracy EEG Biometrics Identification Using ICA and AR Model. Journal of Information and Communication Technology (JICT), 16 (2). pp. 354-373. ISSN 1675-414X

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Abstract

Modern biometric identification methods combine interdisciplinary approaches to enhance person identification and classification accuracy. One popular technique for this purpose is Brain-Computer Interface (BCI). The signal so obtained from BCI will be further processed by the Autoregressive (AR) Model for feature extraction. Many researches in the area find that for more accurate results, the signal must be cleaned before extracting any useful feature information. This study proposes Independent Component Analysis (ICA), k-NN classifier, and AR as the combined techniques for electroencephalogram (EEG) biometrics to achieve the highest personal identification and classification accuracy. However, there is a classification gap between using the combined ICA with the AR model and AR model alone. Therefore, this study takes one step further by modifying the feature extraction of AR and comparing the outcome with the proposed approaches in lieu of prior researches. The experiment based on four relevant locations shows that the combined ICA and AR can achieve higher accuracy than the modified AR. More combinations of channels and subjects are required in future research to explore the significance of channel effects and to enhance the identification accuracy.

Item Type:	Article
Uncontrolled Keywords:	electroencephalogram (EEG), Autoregressive (AR), Independent Component Analysis (ICA), Biometrics, feature extraction, person classification
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Computing
Depositing User:	Mrs Nurin Jazlina Hamid
Date Deposited:	01 Jun 2023 10:06
Last Modified:	01 Jun 2023 10:06
URI:	https://repo.uum.edu.my/id/eprint/29513

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