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.

1. Subasi A., M. Ismail Gursoy (2010) EEG signal classification using PCA, ICA, LDA and support vector machines Expert Systems with Applications ,37 ,8659–8666.
2. Adeli, H., Zhou, Z., & Dadmehr, N. (2003). Analysis of EEG records in an epileptic patient using wavelet transform. Journal of Neuroscience Methods, 123, 69–87.
3. Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in timeseries of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64, 061907.
4. Bronzino, J. D. (2000). Principles of electroencephalography (2nd ed.). In J. D.
5. Bronzino (Ed.). The biomedical engineering handbook. Boca Raton: CRC Press LLC.
6. Cao, L. J., Chua, K. S., Chong, W. K., Lee, H. P., & Gu, Q. M. (2003). A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing, 55, 321– 336.
7. D’Alessandro, M., Esteller, R., Vachtsevanos, G., Hinson, A., Echauz, A., & Litt, B. (2003). Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: A report of four patients. IEEE Transactions on Biomedical Engineering, 50(5), 603–615.
8. Duda, R. O., Hart, P. E., & Strok, D. G. (2001). Pattern classification (2nd ed.). John Wiley & Sons.
9. Semmlow, J. L. (2004). Biosignal and biomedical image processing: MATLAB-based applications. New York: Marcel Dekker, Inc..
10. Hyvärinen A. and Oja E., (1997) A fast fixed-point algorithm for independent component analysis, Neural Computing, 9, 1483–1492.
11. Subasi, A. (2007). Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction. Expert Systems with Applications, 32, 227–244 0 500 1000 1500 2000 2500 3000 3500 4000 -10 0 10 Detail D1 0 500 1000 1500 2000 2500 3000 3500 4000 -50 0 50 Detail D2 0 500 1000 1500 2000 2500 3000 3500 4000 -100 0 100 Detail D3 0 500 1000 1500 2000 2500 3000 3500 4000 -100 0 100 Detail D4 0 500 1000 1500 2000 2500 3000 3500 4000 -100 0 100 Detail D5 0 500 1000 1500 2000 2500 3000 3500 4000 -100 0 100 Approximation A5 0 500 1000 1500 2000 2500 3000 3500 4000 -200 0 200 Detail D1 0 500 1000 1500 2000 2500 3000 3500 4000 -1000 0 1000 Detail D2 0 500 1000 1500 2000 2500 3000 3500 4000 -2000 0 2000 Detail D3 0 500 1000 1500 2000 2500 3000 3500 4000 -1000 0 1000 Detail D4 0 500 1000 1500 2000 2500 3000 3500 4000 -1000 0 1000 Detail D5 0 500 1000 1500 2000 2500 3000 3500 4000 -1000 0 1000 Approximation A5

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