Open AccessClassification of EEG Signals using Nonlinear Features and Preprocessing Techniques
Author(s) -
Saneesh Cleatus T,
M. Thungamani
Publication year - 2021
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.e2789.0610521
Subject(s) - pattern recognition (psychology) , electroencephalography , artificial intelligence , support vector machine , preprocessor , skewness , hilbert–huang transform , computer science , standard deviation , entropy (arrow of time) , speech recognition , kurtosis , mathematics , statistics , psychology , computer vision , physics , filter (signal processing) , quantum mechanics , psychiatry
In this paper we study the effect of nonlinearpreprocessing techniques in the classification ofelectroencephalogram (EEG) signals. These methods are used forclassifying the EEG signals captured from epileptic seizureactivity and brain tumor category. For the first category,preprocessing is carried out using elliptical filters, and statisticalfeatures such as Shannon entropy, mean, standard deviation,skewness and band power. K-Nearest Neighbor (KNN) andSupport Vector Machine (SVM) were used for the classification.For the brain tumor EEG signals, empirical mode decompositionis used as a pre-processing technique along with standardstatistical features for the classification of normal and abnormalEEG signals. For epileptic signals we have achieved an averageaccuracy of 94% for a three-class classification and for braintumor signals we have achieved a classification accuracy of 98%considering it as a two class problem.