Abstract: The paper proposes an approach based on higher order statistics and phase synchronization for detection and classification of relevant features in electroencephalographic (EEG) signals recorded during the subjects are performing motor tasks. The method was tested on two different datasets and the performance was evaluated using k nearest neighbor classifier. The results (classification rates higher than 90%) have shown that the method can be used for discriminating right and left motor imagery tasks as an offline analysis for EEG in a brain computer interface system.

Oana-Diana Hrisca-Eva, Madalina-Giorgiana Murariu and Anca Mihela Lazar, “Higher Order Statistics and Phase Synchronization as Features in a Motor Imagery Paradigm” International Journal of Advanced Computer Science and Applications(IJACSA), 12(8), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120815

@article{Hrisca-Eva2021,
title = {Higher Order Statistics and Phase Synchronization as Features in a Motor Imagery Paradigm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120815},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120815},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {8},
author = {Oana-Diana Hrisca-Eva and Madalina-Giorgiana Murariu and Anca Mihela Lazar}
}