Machine Learning based EEG Signal Classification

Author(s):

Anjali Vasant Kadwe , KCT’s Late.G. N. Sapkal College of Engineering, Anjaneri,Nashik

Keywords:

Epilepsy, Extraction of Data from Text File, Frequency Domain Low Pass Filtering, Feature Extraction, Classification of EEG signal

Abstract:

Epilepsy is a neurological disorder which is characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is a commonly used signal for detection of epileptic seizures. The proposed method is based on the classification of EEG signal with the less number of sample and more accurately by using the Matlab software. The Bonn University Data set use in this project provide classification of EEG signal by using the latest transform method. The project consist of Extraction of the data from text file, Frequency domain low pass filtering And Feature extraction by three most recent transform such as Coiflet Transform, Stationary Wavelet Transform (SWT) and Walsh Hadamard Transform (WHT). This transformed signal is the classified by KNN ensemble classification. This project provide an overall classification accuracy of 99%.

Other Details:

Manuscript Id	:	IJSTEV5I12034
Published in	:	Volume : 5, Issue : 12
Publication Date	:	01/07/2019
Page(s)	:	14-20

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