Classification of EEG Signals using Nonlinear Features and Preprocessing Techniques
Saneesh Cleatus T1, Thungamani M2

1Saneesh Cleatus T*, Department of Electronics and Communication Engineering, BMS Institute of Technology and Management Affiliated to Visvesvaraya Technological, Bangalore(Karnataka), India
2Dr. Thungamani M, Department of Computer Science, College of Horticulture, University of Horticultural Sciences, Bangalore(Karnataka), India. 

Manuscript received on June 08, 2021. | Revised Manuscript received on June 15, 2021. | Manuscript published on June 30, 2021. | PP: 297-301 | Volume-10 Issue-5, June 2021. | Retrieval Number: 100.1/ijeat.E27890610521 | DOI: 10.35940/ijeat.E2789.0610521
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Abstract: In this paper we study the effect of nonlinear preprocessing techniques in the classification of electroencephalogram (EEG) signals. These methods are used for classifying the EEG signals captured from epileptic seizure activity and brain tumor category. For the first category, preprocessing is carried out using elliptical filters, and statistical features such as Shannon entropy, mean, standard deviation, skewness and band power. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) were used for the classification. For the brain tumor EEG signals, empirical mode decomposition is used as a pre-processing technique along with standard statistical features for the classification of normal and abnormal EEG signals. For epileptic signals we have achieved an average accuracy of 94% for a three-class classification and for brain tumor signals we have achieved a classification accuracy of 98% considering it as a two class problem. 
Keywords: EEG, EMD, Epilepsy, Brain Tumor
Scope of the Article: Classification