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DOI: 10.14569/IJACSA.2017.080834
PDF

Feature Extraction and Classification Methods for a Motor Task Brain Computer Interface: A Comparative Evaluation for Two Databases

Author 1: Oana Diana Eva
Author 2: Anca Mihaela Lazar

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 8, 2017.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: A comparative evaluation is performed on two databases using three feature extraction techniques and five classification methods for a motor imagery paradigm based on Mu rhythm. In order to extract the features from electroencephalographic signals, three methods are proposed: independent component analysis, Itakura distance and phase synchronization. The last one consists of: phase locking value, phase lag index and weighted phase lag index. The classification of the extracted features is performed using linear discriminant analysis, quadratic discriminant analysis, Mahalanobis distance based on classifier, the k-nearest neighbors and support vector machine. The aim of this comparison is to evaluate which feature extraction method and which classifier is more appropriate in a motor brain computer interface paradigm. The results suggest that the effectiveness of the feature extraction method depends on the classification method used.

Keywords: Brain computer interface; independent component analysis; Itakura distance; phase synchronization; classifiers

Oana Diana Eva and Anca Mihaela Lazar, “Feature Extraction and Classification Methods for a Motor Task Brain Computer Interface: A Comparative Evaluation for Two Databases” International Journal of Advanced Computer Science and Applications(IJACSA), 8(8), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080834

@article{Eva2017,
title = {Feature Extraction and Classification Methods for a Motor Task Brain Computer Interface: A Comparative Evaluation for Two Databases},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080834},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080834},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {8},
author = {Oana Diana Eva and Anca Mihaela Lazar}
}



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.

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