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Cluster Computing
Published in: Cluster Computing 4/2019

20-09-2017

Algorithms benchmarking for removing EOG artifacts in brain computer interface

Author: Wen Qi

Published in: Cluster Computing | Special Issue 4/2019

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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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Metadata
Title
Algorithms benchmarking for removing EOG artifacts in brain computer interface
Author
Wen Qi
Publication date
20-09-2017
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 4/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1157-0

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