Motion Artifacts Correction From EEG and fNIRS Signals Using Novel Multiresolution Analysis

• Published in: IEEE access Vol. 10; pp. 29760 - 29777
• Main Authors: Hossain, Md. Shafayet, Reaz, Mamun Bin Ibne, Chowdhury, Muhammad E. H., Ali, Sawal H. M., Bakar, Ahmad Ashrif A., Kiranyaz, Serkan, Khandakar, Amith, Alhatou, Mohammed, Habib, Rumana
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: canonical correlation analysis (CCA); Correlation analysis; Decomposition; electroencephalogram (EEG); Electroencephalography; Filtering algorithms; Functional near-infrared spectroscopy; functional near-infrared spectroscopy (fNIRS); Infrared spectra; Mathematical models; Medical imaging; Motion artifact; Motion artifacts; Multiresolution analysis; Near infrared radiation; Neurological diseases; Physiology; Principal component analysis; principal component analysis (PCA); Principal components analysis; Signal measurement; Signal processing; Signal to noise ratio; variational mode decomposition (VMD)
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3159155

Physiological signal measurement and processing are increasingly becoming popular in the ambulatory setting as the hospital-centric treatment is moving towards wearable and ubiquitous monitoring. Most of the physiological signals are highly susceptible to various types of noises, especially movement artifacts. The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals are no exception to motion artifacts, which become prominent in the ambulatory setting. Since successful detection of various neurological disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove motion artifacts from these two signal modalities using reliable and robust methods. This paper proposes three novel multiresolution analysis techniques: i) Variational mode decomposition (VMD), ii) VMD in combination with principal component analysis (VMD-PCA), and iii) VMD in combination with canonical correlation analysis (VMD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these novel techniques is validated by computing the difference in the signal to noise ratio (<inline-formula> <tex-math notation="LaTeX">\Delta SNR </tex-math></inline-formula>) and percentage reduction in motion artifacts (<inline-formula> <tex-math notation="LaTeX">\eta </tex-math></inline-formula>). Among the three proposed novel methods, VMD-CCA decomposed with 15 intrinsic mode functions (IMFs) has shown the best denoising performance for EEG signals producing an average <inline-formula> <tex-math notation="LaTeX">\Delta SNR </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">\eta </tex-math></inline-formula> values of 23.81 dB and 57.01%, respectively for all 23 EEG recordings. On the other hand, for the available 16 fNIRS recordings, VMD-CCA decomposed with 10 IMFs produced an average <inline-formula> <tex-math notation="LaTeX">\Delta SNR </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">\eta </tex-math></inline-formula> values of 15.97 dB and 39.01%, respectively. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.