28, May 2025

Machine Learning Approach for Electroencephalogram (EEG) Signal Classification

VOLUME-9; ISSUE-5; MAY-2025

Author(s): Mr. Akshay Jagtap, Dr. N.S. Bagal, Prof. Poonam Jadhavar

Authors Affiliations:

1Students, M.E., Computer Science Department, Padmabhooshan Vasantdada Patil Institute of Technology, Pune Maharashtra, INDIA

2Professor, Computer Science Department, Padmabhooshan Vasantdada Patil Institute of Technology, Pune Maharashtra, INDIA

3Professor, Computer Science Department, Padmabhooshan Vasantdada Patil Institute of Technology, Pune Maharashtra, INDIA

 

DOIs:10.2017/IJRCS/202505016     |     Paper ID: IJRCS202505016


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Machine learning based EEG signal classification involves utilization of various ML algorithms to provide EEG signal categorization based on different cognitive brain activities. The EEG Signal Classification process is a predominant process used in clinical and biomedical research and brain-computer interface (BCI) system. The machine learning approach in this process provides better EEG signal classification that helps diagnosis faster and accurate. This paper proposed a comparative study between the machine learning based classifiers. Support Vector Machine (SVM) and K-nearest Neighbor (KNN) algorithms are implemented to classify the normal and abnormal EEG signals. 45 subjects are used as a dataset which consist of normal EEG signals and epilepsy (abnormal) signals. The performance parameters of the SVM and KNN classifiers are presented. The accuracy for the EEG signal classification is compared. The SVM based classifier provides better accuracy than the KNN classifier.

Classification, Support Vector Machine, K-nearest Neighbor, EEG SIgnals, Accuracy

Mr. Akshay Jagtap, Dr. N.S. Bagal, Prof. Poonam Jadhavar (2025); Machine Learning approach for Electroencephalogram (EEG) Signal classification, International Journal of Research Culture Society,    ISSN(O): 2456-6683,  Volume – 9,   Issue –  5,  Pp.99-102.        Available on – https://ijrcs.org/

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