Application of LMS adaptive predictive filtering for muscle artifact (noise) cancellation from EEG signals
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Cited by (20)
A Novel SSA-CCA Framework forMuscle Artifact Removal from Ambulatory EEG
2022, Virtual Reality and Intelligent HardwareHybrid wavelet and EMD/ICA approach for artifact suppression in pervasive EEG
2016, Journal of Neuroscience MethodsCitation Excerpt :analyzing quantitative performance of these algorithms on EEG signals corrupted with a larger variety of (eight movement-related) real-life artifacts. Linear filtering and linear regression can separate an artifact from a corrupted signal if the artifact alone is measurable (Narasimhan and Dutt, 1996), e.g., eye-blinking artifact removal using EOG, muscle artifact removal using electromyogram (EMG) and using electrocardiogram (ECG) recoding for the artifact due to heart-beat (Sanei and Chambers, 2008; Sörnmo and Laguna, 2005; Narasimhan and Dutt, 1996; Fatourechi et al., 2007; Moretti et al., 2003). In a pervasive EEG scenario, there is no known way to record the sources of artifact because of the higher degrees of freedom, so as to pose the artifact removal problem in linear filtering template (Sörnmo and Laguna, 2005).
A new approach to robust, weighted signal averaging
2015, Biocybernetics and Biomedical EngineeringCitation Excerpt :The MA noise is typically very non-stationary in time and colored in spectrum (having long-term correlations) and with varying signal-to-noise ratios (SNRs). The muscle noise is the result of superposition of large number of action potentials which form in muscles and such bio-waveform coexists usually with ECG or other signals as noisy components [29]. During the analyzing of muscle noise, one can observe some number of the samples, for which values are significantly far from the average value of the samples.
Audio quality improvement of vehicular hands-free communication using variable step-size affine-projection algorithm
2010, International Journal of Wavelets, Multiresolution and Information ProcessingA multi-step blind source separation approach for the attenuation of artifacts in mobile high-density electroencephalography data
2021, Journal of Neural Engineering
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Present address: Aerospace Electronics Division, National Aerospace Laboratories, Bangalore 560017, India.