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Algorithms benchmarking for removing EOG artifacts in brain computer interface

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Abstract

In this paper, the author investigates and compares two types of algorithms for removing EOG artifacts in EEG measurements for brain computer interface applications. First, the author describes the sources that introduce EOG artifacts and briefly reviews the existing algorithms for EOG artifacts removal in EEG signals. Two selected algorithms—recursive least square (RLS) and blind source separation (BSS) are discussed in details. The algorithms were chosen either for its good performance or for its low computational charge to be used in ambulatory monitoring devices that are developed within our research program. In the third part, the experimental protocol to collect EOG and EEG measurements are described. The forth part summarizes the results after applying two algorithms to the collected EEG signals. From these results, we draw conclusions on the performance of these selected algorithms. Several possible improvements or challenges are proposed in the discussion.

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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. Algorithms benchmarking for removing EOG artifacts in brain computer interface. Cluster Comput 22 (Suppl 4), 10119–10132 (2019). https://doi.org/10.1007/s10586-017-1157-0

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  • DOI: https://doi.org/10.1007/s10586-017-1157-0

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