Machine Learning Verdict of EEG Signals in Brain Computer Interface

Authors

  • M. Jeyanthi  Department of Computer Science, Aditanar College of Arts and Science, Tiruchendur, affiliated to Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India
  • Dr. C. Velayutham  Department of Computer Science, Aditanar College of Arts and Science, Tiruchendur, affiliated to Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India

Keywords:

BCI, classification, KNN, SVM, Data mining

Abstract

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naive Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-12-30

Issue

Volume 3, Issue 8, November-December-2018

Section

Research Articles

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How to Cite

[1]
M. Jeyanthi, Dr. C. Velayutham, " Machine Learning Verdict of EEG Signals in Brain Computer Interface, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 8, pp.429-441, November-December-2018.