EMG contamination of EEG: spectral and topographical characteristics.

By I. Goncharova, Dennis J. McFarland, T. M. Vaughan, J. Wolpaw. Clinical Neurophysiology, 2003

708 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/a6873ec6d96556bf8692f896fff2464ee338245d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Cognitive Neuroscience

Citations in the corpus

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “While EMG artifacts can be partially filtered out from EEG recordings by removing high-frequency signals (46, 311), mechanical artifacts strongly affect the EEG low-frequency range (331).”
Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection (2016, background): “The facial muscular artifacts and eye movements are usually more present in the peripheral electrodes and higher frequencies (beta and gamma bands) [40].”
Towards an independent brain-computer interface using steady state visual evoked potentials. (2008, background): “This right temporal activity is probably EMG noise because of its spatial distribution and the fact that power spectra over that region revealed strong broadband high frequency activity consistent with EMG (Allison, 2003; Goncharova et al., 2003; McFarland et al., 2005).”
Removal of Artifacts from EEG Signals: A Review (2019, background): “Contamination of EEG data by muscle activity is a well-recognized tough problem as it arises from different type of muscle groups [27,29]. type of muscle groups [27,29].”
EEG artifact removal—state-of-the-art and guidelines (2015, background): “Finally, EMG also exhibits less repetition than other biological artifacts and is thus more difficult to characterize, since it arises from the activity of spatially distributed, functionally independent muscle groups, with distinct topographic and spectral signatures [24].”
Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram (2006, background): “Due to the broad frequency spectrum of EMG contamination in scalp EEG recordings [2], muscle artifacts yield more properties of temporally white noise, thus having a low autocorrelation.”
Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery (2011, background): “[35] I.”
Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals (2011, background): “In contrast, EMG activity (caused by chewing, swallowing, head or tongue movements) is usually a high-frequency activity (>20 Hz) which ranges from rather small to very large amplitudes [4].”
Brain–computer interface systems: progress and prospects (2007, background): “BCI studies have improved understanding of disease pathology and patient psychology [6,12,59,60,143,149–151] and addressed EMG and EEG interactions [134,152] .”
DEAP: A Database for Emotion Analysis ;Using Physiological Signals (2012, background): “However, it should be mentioned that the EMG (muscle) activity is also prominent in the high frequencies, especially over anterior and temporal electrodes [46].”
Brain-computer interface technology: a review of the Second International Meeting. (2003, background): “Face movements (e.g., eye or tongue) and breathing may cause considerable artifacts in slow cortical potentials, and, thus, have to be either prevented, or recognized and removed [4].”
Diagnosis of Alzheimer's disease from EEG signals: where are we standing? (2010, background): “Several studies have shown, however, that the frequency range of EMG is in fact much wider than commonly believed [25, 26, 27, 24]:”