Key result
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.
Why the study?
Detection of neurological disorders relies on clean EEG and fNIRS signals, which are susceptible to prominent motion artifacts in ambulatory settings that require reliable removal methods.
Population
23 EEG recordings and 16 fNIRS recordings
Comparison
VMD vs VMD-PCA vs VMD-CCA
Design
Signal processing validation study
Authors
Loading...
May aid motion-artifact removal in research EEG/fNIRS; leaves open clinical validation before practice change.
VMD-CCA is a highly effective multiresolution analysis technique for removing motion artifacts from single-channel EEG and fNIRS signals, outperforming existing methods.
Hossain et al. (2022) studied 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.
Synapse has enriched 3 closely related papers on similar clinical questions. Consider them for comparative context: