• SK EBRAHEEM KHALEELULLA
• PROF P RAJESH KUMAR

Epilepsy is one of the most widely recognized a neurological issue of the cerebrum that influence a large number of the world's population. It is characterized by recurrent seizures, which are physical responses to abrupt, typically short, extreme electrical releases in a group of brain cells. Thus seizure identification has extraordinary significance in clinical treatment of epileptic patients. Electroencephalogram (EEG) is most normally used in epilepsy discovery since it incorporates valuable physiological data of the brain. In any case, it could be a test to identify the inconspicuous yet basic changes incorporated into EEG signals. Feature extraction of EEG signal is center inconvenience on EEG-based brain mapping examination. This paper will extract forty four features from EEG signal in view of variataional mode decomposition (VMD) for epilepsy detection. These various features will assist the classifiers with achieving a decent accuracy when use to arrange EEG signal to distinguish epilepsy. Subsequently, the results have illustrated that VMD has been adopted to extract various features.

Electroencephalogram, Variational Mode Decomposition.

"Feature Extraction of EEG Signals Using Variational Mode Decomposition ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.376-381, June 2019, Available :http://www.jetir.org/papers/JETIR1907513.pdf

"Feature Extraction of EEG Signals Using Variational Mode Decomposition ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp376-381, June 2019, Available at : http://www.jetir.org/papers/JETIR1907513.pdf