This paper is published in Volume-1, Issue-1, 2015
Area
Data Mining
Author
Neelam Rout
Org/Univ
SOA University, ITER College, Rajasthan, India
Paper ID
V1I1-1140
Publisher

Citationsacebook

IEEE
Neelam Rout. Classifications & Misclassifications of EEG Signals using Linear and Ada Boost Support Vector Machines, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Neelam Rout (2015). Classifications & Misclassifications of EEG Signals using Linear and Ada Boost Support Vector Machines. International Journal of Advance Research, Ideas and Innovations in Technology, 1(1) www.IJARIIT.com.

MLA
Neelam Rout. "Classifications & Misclassifications of EEG Signals using Linear and Ada Boost Support Vector Machines." International Journal of Advance Research, Ideas and Innovations in Technology 1.1 (2015). www.IJARIIT.com.

Abstract

Epilepsy is one of the frequent brain disorder due to transient and unexpected electrical interruptions of brain. Electroencephalography (EEG) is one of the most clinically and scientifically exploited signals recorded from humans and very complex signal. EEG signals are non-stationary as it changes over time. So, discrete wavelet transform (DWT) technique is used for feature extraction. Classifications and misclassifications of EEG signals of linearly separable support vector machines are shown using training and testing datasets. Then AdaBoost support vector machine is used to get strong classifier.

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