2-3 September 2021
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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Classification of Non-Discriminant ERD/ERS Comprising Motor Imagery Electroencephalography Signals
Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110146
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 1, 2020.
Abstract: Classification of Motor Imagery (MI) Electroencephalography (EEG) signals has always been an important aspect of Brain Computer Interface (BCI) systems. Event Related Desynchronization (ERD)/ Event Related Synchronization (ERS) plays a significant role in finding discriminant features of MI EEG signals. ERD/ERS is one type and Evoked Potential (EP) is another type of brain response. This study focuses upon the classification of MI EEG signals by Removing Evoked Potential (REP) from non-discriminant MI EEG data in filter band selection, called REP. This optimization is done to enhance the classification performance. A comprehensive comparison of several pipelines is presented by using famous feature extraction methods, namely Common Spatial Pattern (CSP), XDawn. The effectiveness of REP is demonstrated on the PhysioNet dataset which is an online data resource. Comparison is done between the performance of pipelines including proposed one (Common Spatial Pattern (CSP) and Gaussian Process Classifier (GPC)) as well as before and after applying REP. It is observed that the REP approach has improved the classification accuracy of all the subjects used as well as all the pipelines, including state of the art algorithms, up to 20%.
Keywords: MI EEG Signals; non-discriminant ERD/ERS; evoked potentials; common spatial pattern; Gaussian process classifier
Zaib unnisa Asi, M. Sultan Zia, Umair Muneer Butt, Aneela Abbas and Sadaf Ilyas, “Classification of Non-Discriminant ERD/ERS Comprising Motor Imagery Electroencephalography Signals” International Journal of Advanced Computer Science and Applications(IJACSA), 11(1), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110146
@article{Asi2020,
title = {Classification of Non-Discriminant ERD/ERS Comprising Motor Imagery Electroencephalography Signals},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110146},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110146},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {1},
author = {Zaib unnisa Asi and M. Sultan Zia and Umair Muneer Butt and Aneela Abbas and Sadaf Ilyas}
}
