EEG Signal Classification using K-Means and Fuzzy C Means Clustering Methods

Author(s):

Nitin Narahari Hegde , R V College of Engineering; Mr. Harsha, RVCE; Prof. M. S. Nagananda, DSCE

Keywords:

EEG, Classifiers, k-Means Clustering, Fuzzy C Means Clustering, Neural Network Classifiers, Neurointelligence

Abstract:

The Electroencephalogram (EEG) signal is a voltage signal arising from synchronized neural activity. EEG can be used to classify different mental states and to find abnormalities in neural activity. To check the abnormality in neural activity, EEG signal is classified using classifiers. In this project k-means clustering and fuzzy c means (FCM) clustering is used to cluster the input data set to Neural network. NeuroIntelligence is a neural network tool used to classify unknown data points. The non linear time series (NLTS) data set is initially clustered into Normal or Abnormal categories using k-means or FCM clustering methods. This clustered data set is used to train neural network. When an unknown EEG signal is taken, first NLTS measurements are extracted and input to trained neural network to classify the EEG signal. This method of classification proposed is unique and is very easy to classify EEG signals.

Other Details:

Manuscript Id	:	IJSTEV2I1049
Published in	:	Volume : 2, Issue : 1
Publication Date	:	01/08/2015
Page(s)	:	83-87

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