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Classification of EEG using PCA, ICA and Neural Network

Author(s): Kavita Mahajan | M. R. Vargantwar | Sangita M. Rajput

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: iccia;
Issue: 6;
Date: 2012;
Original page

Keywords: Electroencephalogram (EEG) | Principal component analysis (PCA) | Independent components analysis (ICA) | DWT | ANN

The processing and analysis of Electroencephalogram (EEG) within a proposed framework has been carried out with DWT for decomposition of the signal into its frequency sub-bands and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. Reduction of the dimension of the data is done with the help of Principal component analysis and Independent components analysis. Then these features were used as an input to a neural network for classification of the data as normal or otherwise. The performance of classification process due to different methods is presented and compared to show the excellent of classification process. These findings are presented as an example of a method for training, and testing a normal and abnormal prediction method on data from individual petit mal epileptic patients.
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