Channel Selection for Motor Imagery Task Classification using Non-linear Separability Measurement

Stuti Chug, Vandana Agarwal

2022

Abstract

The EEG based motor imagery task classification requires only those channels which contribute to the maximum separability of the training data of different classes. The irrelevant channels are therefore not considered in the formation of feature vectors used in classification. In this paper, we propose a novel algorithm for efficient channel selection (NLMCS). The algorithm computes the proposed metric λ for non-linearity measurement (NLM) and uses this for channel selection. The algorithm is validated on the benchmarked BCI competition IV datasets IIa and IIb. The selected channels are then used for extracting Haar wavelet features and subjected for classification using Support vector Machine. The minimum value of λ corresponds to the optimal channel selection resulting in the best accuracy of motor imagery task classification. The mean Kappa coefficient computed for BCI competition IV IIa dataset using the proposed algorithm is 0.65 and it outperforms some existing approaches.

Paper Citation

in Harvard Style

Chug S. and Agarwal V. (2022). Channel Selection for Motor Imagery Task Classification using Non-linear Separability Measurement. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-549-4, pages 171-178. DOI: 10.5220/0010787300003122

in Bibtex Style

@conference{icpram22,
author={Stuti Chug and Vandana Agarwal},
title={Channel Selection for Motor Imagery Task Classification using Non-linear Separability Measurement},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2022},
pages={171-178},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010787300003122},
isbn={978-989-758-549-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Channel Selection for Motor Imagery Task Classification using Non-linear Separability Measurement
SN - 978-989-758-549-4
AU - Chug S.
AU - Agarwal V.
PY - 2022
SP - 171
EP - 178
DO - 10.5220/0010787300003122