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Research Article | Open Access |

Higher Order Statistics and Phase Synchronization as Features in a Motor Imagery Paradigm

Author 1: Oana-Diana Hrisca-Eva Author 2: Madalina-Giorgiana Murariu Author 3: Anca Mihela Lazar
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 12, No. 8 · Published 2021

DOI: https://doi.org/10.14569/IJACSA.2021.0120815

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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.

Keywords

Brain computer interface motor imagery higher order statistics phase synchronization EEG

How to Cite this Article

Hrisca-Eva, O., Murariu, M., & Lazar, A. M. (2021). Higher Order Statistics and Phase Synchronization as Features in a Motor Imagery Paradigm. International Journal of Advanced Computer Science and Applications, 12(8). https://doi.org/10.14569/IJACSA.2021.0120815

Hrisca-Eva, Oana-Diana, et al.. "Higher Order Statistics and Phase Synchronization as Features in a Motor Imagery Paradigm." International Journal of Advanced Computer Science and Applications, vol. 12, no. 8, 2021, https://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},
  volume    = {12},
  number    = {8},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Oana-Diana Hrisca-Eva and Madalina-Giorgiana Murariu and Anca Mihela Lazar},
  doi       = {10.14569/IJACSA.2021.0120815},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120815}
}

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