COI code: ISCEE14_074
Paper Language: English
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Authors EEG Signals Classification Based on Wavelet Transform and a New Statistical FeatureSara Mihandoost - Department of Electrical Engineering, Urmia University, Urmia, Iran
Noorieh Omidi - 2Department of Education Technology, Azad Eslami University, Kermanshah, Iran
Abstract:In this paper, we use a new set of statistic feature for the Electroencephalogram (EEG) signals classification. The EEG signals are decomposed into the frequency sub-bands using discrete wavelet transform (DWT). A set of statistical features is extracted from each sub-band to represent the distribution of wavelet coefficients. We propose three new statistical features, Fourth moment, betwixt maximum and minimum and zero-crossing. These features cause to improve Correct Classification rate (CCR). Next, we use a linear discriminant analysis (LDA) and Principal component analysis (PCA) for decrease the dimension of features. Then these features are classified by multilayer perceptron (MLP) with three discrete outputs: healthy volunteers, epilepsy patients during seizure-free interval and epilepsy patients during seizure. Experimental results on a set of EEG signals from Andrzejak et al (2001) data base show a good performance achieved by the proposed method in comparison with some recent methods
Keywords:EEG signal, DWT, LDA, PCA, and MLP
COI code: ISCEE14_074
how to cite to this paper:If you want to refer to this article in your research, you can easily use the following in the resources and references section:
Mihandoost, Sara & Noorieh Omidi, 2011, EEG Signals Classification Based on Wavelet Transform and a New Statistical Feature, 14th Iranian Student Conference on Electrical Engineering, كرمانشاه, دانشگاه كرمانشاه, سازمان علمي دانشجويي مهندسي برق كشور, https://www.civilica.com/Paper-ISCEE14-ISCEE14_074.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Mihandoost, Sara & Noorieh Omidi, 2011)
Second and more: (Mihandoost & Omidi, 2011)
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Type: state university
Paper No.: 11276
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