Author : Rohini Darade 1
Date of Publication :11th August 2017
Abstract: Human brain is a diverse creature, and unveils rich spatiotemporal dynamics. Among the noninvasive techniques for probing human brain dynamics, electroencephalography (EEG) provides a direct measure of cortical activity with millisecond temporal resolution. Electroencephalogram is a signal produced in the human brain when there is an information flow among several neurons. Human brain contains millions of neurons which are responsible for information flow. We have classified the publically available dataset for testing between normal and epileptic persons. We have achieved accuracy of 99.88% which is highest accuracy on this dataset.
Reference :
-
[1] Y. Ma, X. Ding, Q. She, Z. Luo, T. Potter, and Y. Zhang, "Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization," Computational and Mathematical Methods in Medicine, vol. 2016, p. 8, 2016.
[2] A. Subasi, "EEG signal classification using wavelet feature extraction and a mixture of expert model," Expert Systems with Applications, vol. 32, pp. 1084-1093, 5// 2007.
[3] D. Wang, D. Miao, C. Xie, " Expert Systems with Applications,‖The Best basis based absolute wavelet packet entropy Expert Systems with Applications feature extraction and hierarchical EEG classification for epileptic detection," Expert Systems with Applications, vol. 38, pp. 14314-14320, 10// 2011.
[4] L. Guo, D. Rivero, J. Dorado, C. R. Munteanu, and A. Pazos, "Automatic feature extraction using genetic programming: An application to epileptic EEG classification," Expert Systems with Applications, vol. 38, pp. 10425-10436, 8// 2011.
[5] P. Jahankhani, V. Kodogiannis, K. Revett, "The EEG Symposium on Modern Computing Signal and Classification Using the Wavelet Feature Extraction and Neural Networks," in IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA'06), 2006, pp. 120-124.
[6] H. U. Amin, A. S. Malik, R. F. Ahmad, N. Badruddin, N. Kamel, M. Hussain, et al., "Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques," Australasian Physical & Engineering Sciences in Medicine, vol. 38, pp. 139-149, 2015.Physical & Engineering Sciences in Medicine, vol. 38, pp. 139- 149, 2015.
[7] Andrzejak RG, Lehnertz K, Rieke C, Mormann F, David P, Elger CE (2001) Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state, Phys. Rev. E, 64, 061907
[8] Sandeep Kumar Satapathy, Satchidananda Dehuri, Alok Kumar Jagadev, ABC optimized RBF network for classification of EEG signal for epileptic seizure identification, Egyptian Informatics Journal, Volume 18, Issue 1, March 2017, Pages 55-66