inis 24(2): e5

Research Article

Real-time Single-Channel EOG removal based on Empirical Mode Decomposition

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  • @ARTICLE{10.4108/eetinis.v11i2.4593,
        author={Kien Nguyen Trong and Nhat Nguyen Luong and Hanh Tan and Duy Tran Trung and Huong Ha Thi Thanh and Duy Pham The and Binh Nguyen Thanh},
        title={Real-time Single-Channel EOG removal based on Empirical Mode Decomposition},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        keywords={Empirical mode composition, Electrooculogram artifacts, single-channel artefact removal},
  • Kien Nguyen Trong
    Nhat Nguyen Luong
    Hanh Tan
    Duy Tran Trung
    Huong Ha Thi Thanh
    Duy Pham The
    Binh Nguyen Thanh
    Year: 2024
    Real-time Single-Channel EOG removal based on Empirical Mode Decomposition
    DOI: 10.4108/eetinis.v11i2.4593
Kien Nguyen Trong1,*, Nhat Nguyen Luong1, Hanh Tan1, Duy Tran Trung1, Huong Ha Thi Thanh2, Duy Pham The1, Binh Nguyen Thanh1
  • 1: Posts and Telecommunications Institute of Technology
  • 2: Ho Chi Minh City International University
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In recent years, single-channel physiological recordings have gained popularity in portable health devices and research settings due to their convenience. However, the presence of electrooculogram (EOG) artifacts can significantly degrade the quality of the recorded data, impacting the accuracy of essential signal features. Consequently, artifact removal from physiological signals is a crucial step in signal processing pipelines. Current techniques often employ Independent Component Analysis (ICA) to efficiently separate signal and artifact sources in multichannel recordings. However, limitations arise when dealing with single or a few channel measurements in minimal instrumentation or portable devices, restricting the utility of ICA. To address this challenge, this paper introduces an innovative artifact removal algorithm utilizing enhanced empirical mode decomposition to extract the intrinsic mode functions (IMFs). Subsequently, the algorithm targets the removal of segments related to EOG by isolating them within these IMFs. The proposed method is compared with existing single-channel EEG artifact removal algorithms, demonstrating superior performance. The findings demonstrate the effectiveness of our approach in isolating artifact components, resulting in a reconstructed signal characterized by a strong correlation and a power spectrum closely resembling the ground-truth EEG signal. This outperforms the existing methods in terms of artifact removal. Additionally, the proposed algorithm exhibits significantly reduced execution time, enabling real-time online analysis.