Comparing features extraction methods for person authentication using EEG signals

Liew, Siaw Hong and Choo, Yun Huoy and Low, Yin Fen and Mohd Yusoh, Zeratul Izzah and Yap, Tian Bee and Draman @ Muda, Azah Kamilah (2014) Comparing features extraction methods for person authentication using EEG signals. In: WICT'14, 8-10 December 2014, Melaka. (In Press)

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This paper presents a comparison and analysis of six feature extraction methods which were often cited in the literature, namely wavelet packet de-composition (WPD), Hjorth parameter, mean, coherence, cross-correlation and mutual information for the purpose of person authentication using EEG signals. The experimental dataset consists of a selection of 5 lateral and 5 midline EEG channels extracted from the raw data published in UCI reposi-tory. The experiments were designed to assess the capability of the feature extraction methods in authenticating different users. Besides, the correlation-based feature selection (CFS) method was also proposed to identify the sig-nificant features subset and enhance the authentication performance of the features vector. The performance measurement were based on the accuracy and area under ROC curve (AUC) values using the fuzzy-rough nearest neighbour (FRNN) classifier proposed previously in our earlier work. The results show that all the six feature extraction methods are promising. How-ever, WPD will induce large vector set when the selected EEG channels in-creases. Thus, the feature selection process is important to reduce the fea-tures set before combining the significant features with the other small fea-ture vectors set.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: extraction, authentication, EEG signals electroencephalograms, feature extraction, person authentication, feature selection
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Information and Communication Technology > Department of Industrial Computing
Depositing User: Dr. Yun-Huoy Choo
Date Deposited: 20 Jan 2015 03:33
Last Modified: 23 May 2023 12:20
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