VMD-CCA demonstrated superior denoising performance for EEG signals, producing an average ΔSNR of 23.81 dB and a 57.01% reduction in motion artifacts.
VMD-CCA is a highly effective multiresolution analysis technique for removing motion artifacts from single-channel EEG and fNIRS signals, outperforming existing methods.
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 (SNR) and percentage reduction in motion artifacts (). 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 SNR and 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 SNR and 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.
Hossain et al. (Sat,) conducted a other in Motion artifacts in EEG and fNIRS signals. VMD-CCA (Variational mode decomposition with canonical correlation analysis) vs. Existing state-of-the-art techniques was evaluated on Difference in signal to noise ratio (ΔSNR) and percentage reduction in motion artifacts (η). VMD-CCA demonstrated superior denoising performance for EEG signals, producing an average ΔSNR of 23.81 dB and a 57.01% reduction in motion artifacts.
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