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Evaluating algorithms of removing EOG artifacts with experimental data in brain computer interface

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Abstract

In this paper, the authors investigate and compare two types of algorithms for removing EOG artifacts in EEG measurements. First, the sources that introduce EOG artifacts are explained and the authors briefly review the existing algorithms for EOG artifacts removal in EEG signals. Two selected algorithms—recursive least square and blind source separation are discussed in details. These two algorithms were chosen either for their good performance or for their low computational charge to be used in ambulatory monitoring devices. In the third part, the experimental protocol to collect EOG and EEG measurements are described. In the fourth part, the authors summarize the results after applying two algorithms to the collected real EEG data. From these results, conclusions are drawn on the performances of these selected algorithms. Several possible improvements or challenges are proposed in the discussion section.

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Acknowledgements

The authors would like to thank the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning (No. TP2015029) for financial support.

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  1. Donghua University, Shanghai, China

    Wen Qi

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  1. Wen Qi
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Correspondence to Wen Qi.

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Qi, W. Evaluating algorithms of removing EOG artifacts with experimental data in brain computer interface. Cluster Comput 22 (Suppl 4), 10371–10384 (2019). https://doi.org/10.1007/s10586-017-1555-3

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  • DOI: https://doi.org/10.1007/s10586-017-1555-3

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