Artifact Removal Methods in EEG Recordings: A Review

  • Mariyadasu Mathe Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India; Gandhi Institute of Technology and Management, Andhra Pradesh, India
  • Padmaja Mididoddi Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Andhra Pradesh, India
  • Battula Tirumala Krishna Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India
Keywords: EEG signal, canonical correlation analysis (CCA), SSA-BSS, EMD-IVA

Abstract

To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods.

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Published
2021-08-24
How to Cite
Mathe, M., Mididoddi, P., & Krishna, B. T. (2021). Artifact Removal Methods in EEG Recordings: A Review. Proceedings of Engineering and Technology Innovation. https://doi.org/10.46604/peti.2021.7653
Section
Articles