We propose in this paper a feature extraction method to extract brain wave features from electroencephalography (EEG) signal. The proposed feature extraction method is based on an assumption that EEG signal could be considered as stationary if the time window is sufficiently short. With this assumption, EEG signal has some similar properties to speech signal and hence a feature extraction method that is currently used to extract speech features can be applied to extract brain wave features from EEG signal. Mel-frequency cepstral coefficients are features extracted and evaluated in EEG-based person identication. Experimental results show that the proposed method could provide very high recognition rate.
|Title of host publication||ICAI 2012 : Proceedings of the 2012 International Conference on Artificial Intelligence, WORLDCOMP'12 : Volume II|
|Editors||Hamid Reza Arabnia|
|Place of Publication||USA|
|Number of pages||6|
|Publication status||Published - 2012|
|Event||2012 International Conference on Artificial Intelligence - Las Vegas, Las Vegas, United States|
Duration: 16 Jul 2012 → 19 Jul 2012
|Conference||2012 International Conference on Artificial Intelligence|
|Abbreviated title||ICAI 2012|
|Period||16/07/12 → 19/07/12|
Tran, D., Huang, X., & Sharma, D. (2012). A Proposed Feature Extraction Method for EEG-based Person Identification. In H. R. Arabnia (Ed.), ICAI 2012 : Proceedings of the 2012 International Conference on Artificial Intelligence, WORLDCOMP'12 : Volume II (Vol. 2, pp. 826-831). USA: CSREA Press.