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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Abstract
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 |
| URI: | http://eprints.utem.edu.my/id/eprint/14030 |
| Statistic Details: | View Download Statistic |
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