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Comparison of two methods of removing EOG artifacts for use in a motor imagery-based brain computer interface

  • M. Mohammadi
  • M. R. MosaviEmail author
Original Paper
  • 15 Downloads

Abstract

Blind Source Separation (BSS) methods are signal processing tools that are widely utilized for Electroencephalogram (EEG) data analysis. The BSS separates a set of recorded EEG signals into a set of components. Note that the EEG measurements are often contaminated with various types of artifacts such as eye movements and blinks (Electrooculogram, EOG) that make data analysis very difficult. Therefore, we present a comparative study between two methods of removing EOG artifacts to be use in a Motor Imagery-based Brain Computer Interface (MI-BCI). In literature, a method of artifact removal based on Independent Component Analysis (ICA) using the concepts of Renyi’s entropy and kurtosis has been presented that we call this method ICA (the first method). However, applying the ICA method to EEG signal directly can cause the significant distortion of the clean EEG signal because this method completely removes independent components capturing artifacts. In this study, we improve this method by applying Wavelet Denoising (WD) only to segments affected by artifacts from the detected artifactual components instead of the complete removal of these components. This method is called ICA–WD (the second method). The quality of removing the artifact is evaluated using the Relative Root-Mean-Squared Error and Average Correlation Coefficient criteria which are computed between the original and processed signals. The results show that the ICA–WD removes the EOG artifacts better than the ICA. Next, we apply the ICA–WD method to dataset 2a of BCI competition IV for the EOG artifact removal. Then, the cleaned EEG signals are employed for feature extraction and classification. The results indicate that the proposed method outperforms the three best competitors and another study performed on the BCI competition IV dataset 2a.

Keywords

Blind source separation Artifact removal Independent component analysis Brain computer interface Classification 

Notes

Supplementary material

12530_2019_9311_MOESM1_ESM.pdf (2.1 mb)
Supplementary material 1 (PDF 2105 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringIran University of Science and TechnologyTehranIran

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