The aim of this paper is to develop the classification system using Artificial Neural Network for Electroencephalogram (EEG) signals. A good standard traditional method is to use Electroencephalogram for diagnosing patients brain functioning that corresponds to epilepsy and different brain disorders. This research focused on designing new classification techniques for single channel EEG recordings. This work proposes three classification techniques namely Back propagation feed forward neural networks (BPFFNN) with different training algorithms, Radial Basis Function Neural Network (RBFNN) and Particle swarm optimization (PSO) based neural network to classify from EEG signals whether a person is epileptic or nonepileptic. The first aspects of proposed work is to extract the features from EEG signals based on statistical measures and evaluate the neural networks architecture with various numbers of hidden neurons to reduce the complexity of the system. Feed forward neural networks have trained using different learning algorithms. Particle swarm optimization techniques proposed with optimal parameters to train the feed forward neural network. The performance of proposed method has compared with other commonly used classification techniques. The BPLM, RBFNN and PSONN provide very promising and practical results and required much less time and memory resources and improved classification accuracy and generalization. This study based on EEG benchmark database and it is publically available source.

1. R. G. Andrezejak, K. Lehnertz, C. Rieke, F. Mormann, P. David, C. E. Elger, "Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state, Phy. Rev. E. , 64,061907. [Online] Available: http://epileptologie-bonn. de/cmsDing, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
2. M. K. Kiymik, A. Subasi, and H. R. Ozcalik, "Neural networks with period gram and autoregressive spectral analysis methods in detection of epileptic seizure,"J. Med. Syst. vol. 28, pp. 511-522, 2004.
3. N. F. Gulera, E. D. Ubeylib, I. Guler, "Recurrent neural networks employing Lyapunov Exponents for EEG signals classification" Expert systems with Application , Vol. 29, pp. 506-514, 2005.
4. Kezban Aslan, Hacer Bozdemir, Cenk Sahin, Seyfettin Noyan Ogulata and Rizvan Erol,"A Radial Basis Function Neural Network Model for classification of Epilepsy using EEG signals," Journal of Medical Systems , Vol. 32 ,pp. 403-408, 2008.
5. Pari Jahankhani, Vassilis Kodogiannis and Kenneth Revett," EEG signal classification using Wavelet feature extraction and neural networks," IEEE computer society, John Vincent Atanasoff 2006 International Symposium on modern computing (JVA'06).
6. V. Srinivasan, Chikkannan Eswaran, "Approximate Entropy –Based Epileptic EEG Detection using Artificial Neural Networks ," IEEE Trans. on Information Tech. in Biomedicine, vol. 11, No. 3, May 2007.
7. Samanwoy Ghosh- Dastidar, Hojjat Adeli, "Principal component Analysis –Enhanced cosign Radial Basis Function Neural Network for Robust Epilepsy and Seizure detection", IEEE Trans. On Biomedical Engg. vol. 55, No. 2, February 2008.
8. R. Xu, G. C. Anagnostopoulos,and Donald C. Wunsch I I," Multiclass Cancer Classification using Semi supervised Ellipsoid ARTMAP and Particle Swarm Optimization with Gene Expression Data", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 4, No. 1,Jan-Mar. 2007.
9. K. Polat, and S. Gunes, "Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform", Appl. Math. Comput. vol. 32 (2), 2007. pp 625-31.
10. A. Subasi," Signal classification using wavelet feature extraction and a mixture of expert model", Exp. Syst. Appl. , vol. 32 (4), 2007, pp. 1084-93.
11. A. T. Tzallas, M. G. Tsipouras, D. I. Fotiadis," A time –frequency based method for the detection of Epileptic Seizures in EEG Recordings" Twentieth IEEE International Symposium on computer –based medical systems (CBMS-07) 2007.
12. A. T. Tzallas, M. G. Tsipouras, D. I. Fotiadis,"Epileptic seizure detection in EEGs using time –frequency analysis, IEEE Tras. Inf. Technol. Biomed" vol. 13, no. 5, pp. 703710. Sep. 2009.
13. M. V. Bedeeuzzaman, O. Farooq, and Y. U. Khan,"Automatic seizure detection using higher order moments, "in Proc. Int. Inf. Recent Trends Inf. , Telecommum. Comput. 2010, pp. 159-163.
14. T. Fathima, Y. U. Khan, M. Bedeeuzzaman, and O. Farooq,"Discriminant analysis for epileptic seizure detection," in Proc. Inf. Conf. Devices Commun. , 2011, pp. 1-5.
15. Samanwoy Ghosh- Dastidar, Hojjat Adeli," Principal component Analysis –Enhanced cosign Radial Basis Function Neural Network for Robust Epilepsy and Seizure detection", IEEE Trans. On Biomedical Engg. vol. 55, No. 2, February 2008.
16. Samanwoy Ghosh- Dastidar, Hojjat Adeli, and Nahid Dadmehr,"Mixed –Band Wavelet-chaos- Neural Network Methodology for Epilepsy and Epileptic Seizure Detection", IEEE Trans. On Biomed. Engg. , vol. 54, No. 9, September 2007.

Back Propagation Neural Network (bpnn) Radial Basis Function (rbfnn) Electroencephalogram (eeg) Artificial Neural Network (ann) Epilepsy Particle Swarm Optimization (pso)