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Removal of EOG Artifact in Electroencephalography with EEMD-ICA: A Semi-simulation Study on Identification of Artifactual Components

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Internet of Things of Big Data for Healthcare (IoTBDH 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2019))

  • 70 Accesses

Abstract

Purpose: The electroencephalography (EEG) signals recorded in clinical settings are usually corrupted by electrooculography (EOG) artifacts. EEMD-ICA is a commonly used method for removing EOG artifacts. This study aims at exploring the performance of different methods of identification of artifactual components under the framework of EEMD-ICA.

Methods: This study is conducted in a semi-simulated way. A EEG dataset covering signal of SNR from -1 to 2 is generated based on the EEG and EOG segments from two public datasets. Characterized by the artifactual components identification method, EEMD-ICA\(_{kurt}\), EEMD-ICA\(_{entropy}\), EEMD-ICA\(_{autocor}\) and EEMD-ICA\(_{eogcor}\) are proposed and evaluated in terms of Normalized Mean Square Error (NMSE), Cross Correlation (CC) and Structural Similarity Index (SSIM) on this dataset.

Results: EEMD-ICA\(_{autocor}\) outperforms other three approaches and demonstrates the strongest versatility. Besides successfully eliminating EOAs from EEG signals, it loses the least neuron activities.

Conclusion: Although performance metrics improve as SNR increases, the loss of structure information also improves (SNR > 1). In practice, it is vital to estimate the SNR of data before applying these approaches because when SNR is high, these methods may have a counterproductive.

Supported by Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 62301452) and Natural Science Foundation of the Jiangsu Higher Education Institutions of China-General Programme (Grant No. 21KJB510024).

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Authors and Affiliations

  1. Xi’an Jiatong -Liverpool University, Renai Rd. 111, 215123, Suzhou, China

    Jingzhou Xu, Wengyao Jiang, Wei Wang, Jianjun Chen, Yixiao Shen & Jun Qi

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  1. Jingzhou Xu
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  2. Wengyao Jiang
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Correspondence to Jun Qi .

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Editors and Affiliations

  1. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Jun Qi

  2. University of Sheffield, Sheffield, UK

    Po Yang

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Xu, J., Jiang, W., Wang, W., Chen, J., Shen, Y., Qi, J. (2024). Removal of EOG Artifact in Electroencephalography with EEMD-ICA: A Semi-simulation Study on Identification of Artifactual Components. In: Qi, J., Yang, P. (eds) Internet of Things of Big Data for Healthcare. IoTBDH 2023. Communications in Computer and Information Science, vol 2019. Springer, Cham. https://doi.org/10.1007/978-3-031-52216-1_10

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  • DOI: https://doi.org/10.1007/978-3-031-52216-1_10

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