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Applying correlation analysis to electrode optimization in source domain

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Abstract

In brain computer interface-based neurorehabilitation system, a large number of electrodes may increase the difficulty of signal acquisition and the time consumption of decoding algorithm for motor imagery EEG (MI-EEG). The traditional electrode optimization methods were limited by the low spatial resolution of scalp EEG. EEG source imaging (ESI) was further applied to reduce the number of electrodes, in which either the electrodes covering activated cortical areas were selected, or the reconstructed electrodes of EEGs with higher Fisher scores were retained. However, the activated dipoles do not all contribute equally to decoding, and the Fisher score cannot represent the correlations between electrodes and dipoles. In this paper, based on ESI and correlation analysis, a novel electrode optimization method, denoted ECCEO, was developed. The scalp MI-EEG was mapped to cortical regions by ESI, and the dipoles with larger amplitudes were chosen to designate a region of interest (ROI). Then, Pearson correlation coefficients between each dipole of the ROI and the corresponding electrode were calculated, averaged, and ranked to obtain two average correlation coefficient sequences. A small but important group of electrodes for each class were alternately added to the predetermined basic electrode set to form a candidate electrode set. Their features were extracted and evaluated to determine the optimal electrode set. Experiments were conducted on two public datasets, the average decoding accuracies achieved 95.99% and 88.30%, and the reduction of computational cost were 65% and 56%, respectively; statistical significance was examined as well.

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Acknowledgements

We would like to thank the provider of the datasets and all of the people who have given us helpful suggestions and advice. The authors are obliged to the anonymous referee for carefully looking over the details and for useful comments which improved this paper.

Funding

This work was financially supported by the National Key Research and Development Program of China (No. 2020YFC2004400) and the National Natural Science Foundation of China (No. 62173010).

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Yuxin Dong, Linlin Wang & Mingai Li

  2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China

    Mingai Li

  3. Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China

    Mingai Li

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Correspondence to Mingai Li.

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Dong, Y., Wang, L. & Li, M. Applying correlation analysis to electrode optimization in source domain. Med Biol Eng Comput 61, 1225–1238 (2023). https://doi.org/10.1007/s11517-023-02770-w

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