Record Details

<strong>Brain wave classification for divergent hand movements</strong>

Online Publishing @ NISCAIR

View Archive Info
 
 
Field Value
 
Authentication Code dc
 
Title Statement <strong>Brain wave classification for divergent hand movements</strong>
 
Added Entry - Uncontrolled Name S, Bagyaraj ; Sri Sivasubramaniya Nadar College of Engineering
S, Apurva ; Sri Sivasubramaniya Nadar College of Engineering
R, Asha ; Sri Sivasubramaniya Nadar College of Engineering
B, Sangeetha ; Sri Sivasubramaniya Nadar College of Engineering
D, Vaithiyanathan ; Assistant Professor, NIT Delhi
 
Uncontrolled Index Term EEG; DWT; ERD/ERS; Classification; SVM; Binary decision tree; Discriminant Analysis; Naïve Bayes
 
Summary, etc. Brain-Computer Interface (BCI) is an emerging technology in medical diagnosis and rehabilitation. In this study, by the acquisition of Electroencephalogram (EEG) signals from 30 healthy participants who perform four different hand movements, necessary features are extracted and classified to determine their accuracies. Statistical time domain features are extracted from the mu and beta frequency band. The Event related desynchronization (ERD)/Event related synchronization (ERS) measurements are extracted, from which it was evident that both mu and beta frequency bands are more efficient in the C3 channel. By applying the Paired Samples <em>t</em>-test, the extracted features are analyzed and were determined to have a 95% significant level of difference between the mu and beta band, being statistically efficient in the beta band of the C3 channel. By employing different classifiers such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Naïve Bayesian classifier and Binary Decision Tree (BDT) algorithms on both channel’s mu and beta frequency bands, it was observed that the performance of beta frequency band classifiers shows 90% accuracy in binary class classification. In the comparative study of all these classifiers, LDA and Naïve Bayes show above 95% accuracy for binary class classification.
 
Publication, Distribution, Etc. Indian Journal of Pure & Applied Physics (IJPAP)
2020-10-20 09:58:39
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/IJPAP/article/view/36644
 
Data Source Entry Indian Journal of Pure & Applied Physics (IJPAP); ##issue.vol## 58, ##issue.no## 10 (2020): Indian Journal of Pure & Applied Physics
 
Language Note en
 
Nonspecific Relationship Entry http://op.niscair.res.in/index.php/IJPAP/article/download/36644/465513707
 
Terms Governing Use and Reproduction Note Except where otherwise noted, the Articles on this site are licensed under Creative Commons License: CC Attribution-Noncommercial-No Derivative Works 2.5 India © 2015. The Council of Scientific &amp; Industrial Research, New Delhi.