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EEG Signals Classification Based on Wavelet Transform and a New Statistical Feature

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Year: 2011
Present: شفاهي
COI code: ISCEE14_074
Paper Language: English

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Authors EEG Signals Classification Based on Wavelet Transform and a New Statistical Feature

  Sara Mihandoost - Department of Electrical Engineering, Urmia University, Urmia, Iran
Noorieh Omidi - 2Department of Education Technology, Azad Eslami University, Kermanshah, Iran


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


EEG signal, DWT, LDA, PCA, and MLP

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COI code: ISCEE14_074

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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, 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.
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The University/Research Center Information:
Type: state university
Paper No.: 11276
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