Abstract

The aim of this study is to use Fuzzy Mutual Information as a Dimensionality Reduction Technique for Epileptic Electroencephalography Signals. To design an effective classification model, it is vital to extract a small set of closely related relevant features from a data set which has a high dimension. Such a type of procedure should explore the series of estimations of the relationship between each and every pair of variables. Also, the estimation is done between the two variables and also for the class labels too. For continuous and hybrid data there are various other strategies that are useful for the estimation of mutual information. Fuzzy Mutual Information is very helpful for obtaining the most stable feature sets and the relationships between two variables is accurately estimated. In this study, Fuzzy Mutual Information is applied as the dimensionality reduction technique for the electroencephalography signals obtained from epileptic patients. The Electroencephalogram (EEG) is actually a measure of the cumulative firing of neurons in various parts of the brain. The EEG contains the information with regard to the changes in the electrical potential of the brain which is obtained from a set of recording electrodes. Here the results are discussed using Fuzzy Mutual Information technique as a dimensionality reduction technique for the processing of electroencephalography signals from an epileptic patient.

Keywords:

EEG , epilepsy , fuzzy mutual information,

References

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Competing interests

The authors have no competing interests.

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The authors have no competing interests.