Removing Cardiac Artefacts in Magnetoencephalography with Resampled Moving Average Subtraction

Sun, Lianna; Ahlfors, Seppo P.; Hinrichs, Hermann

doi:10.1007/s10548-016-0513-3

Cited by 9 publications

(5 citation statements)

References 35 publications

(50 reference statements)

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“…Cardiac artifacts include the direct interference of voltage gradients or magnetic fields generated by the cardiac cycle at cranial sensor locations (Sun et al, 2016), as well as artifacts related to associated cardiovascular (blood flow) processes, often referred to as pulse artifacts (Tamburro et al, 2019). These two types of cardiac artifacts differ in their temporal profile, with vascular artifacts showing a slower, smoother time course and electrical artifacts reflecting the cardiac cycle, thus including a sharp transient deflection corresponding to the R-wave of the electrocardiogram.…”

Section: Cardiac and Respiratory Artifactsmentioning

confidence: 99%

Recommendations and publication guidelines for studies using frequency domain and time‐frequency domain analyses of neural time series

et al. 2022

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Since its beginnings in the early 20th century, the psychophysiological study of human brain function has included research into the spectral properties of electrical and magnetic brain signals. Now, dramatic advances in digital signal processing, biophysics, and computer science have enabled increasingly sophisticated methodology for neural time series analysis. Innovations in hardware and recording techniques have further expanded the range of tools available to researchers interested in measuring, quantifying, modeling, and altering the spectral properties of neural time series. These tools are increasingly used in the field, by a growing number of researchers who vary in their training, background, and research interests. Implementation and reporting standards also vary greatly in the published literature, causing challenges for authors, readers, reviewers, and editors alike. The present report addresses this issue by providing recommendations for the use of these methods, with a focus on foundational aspects of frequency domain and time‐frequency analyses. It also provides publication guidelines, which aim to (1) foster replication and scientific rigor, (2) assist new researchers who wish to enter the field of brain oscillations, and (3) facilitate communication among authors, reviewers, and editors.

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Section: Cardiac and Respiratory Artifactsmentioning

confidence: 99%

Recommendations and publication guidelines for studies using frequency domain and time‐frequency domain analyses of neural time series

et al. 2022

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“…Several methods have been successful in removing cardiac interference from EEG and MEG data (Viola et al, 2009; Sun et al, 2016; Mannan et al, 2018), however, there are still several outstanding issues which present challenges to the field. Although some methods work well in specific contexts (Urrestarazu et al, 2004; Abtahi et al, 2014; Hamaneh et al, 2014; Navarro et al, 2015), wide-spread application across multiple acquisition platforms and conditions remains to be evaluated.…”

Section: Introductionmentioning

confidence: 99%

Automatic Removal of Cardiac Interference (ARCI): A New Approach for EEG Data

2019

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EEG recordings are generally affected by interference from physiological and non-physiological sources which may obscure underlying brain activity and hinder effective EEG analysis. In particular, cardiac interference can be caused by the electrical activity of the heart and/or cardiovascular activity related to blood flow. Successful EEG application in sports science settings requires a method for artifact removal that is automatic and flexible enough to be applied in a variety of acquisition conditions without requiring simultaneous ECG recordings that could restrict movement. We developed an automatic method for classifying and removing both electrical cardiac and cardiovascular artifacts (ARCI) that does not require additional ECG recording. Our method employs independent component analysis (ICA) to isolate data independent components (ICs) and identifies the artifactual ICs by evaluating specific IC features in the time and frequency domains. We applied ARCI to EEG datasets with cued artifacts and acquired during an eyes-closed condition. Data were recorded using a standard EEG wet cap with either 128 or 64 electrodes and using a novel dry electrode cap with either 97 or 64 dry electrodes. All data were decomposed into different numbers of components to evaluate the effect of ICA decomposition level on effective cardiac artifact detection. ARCI performance was evaluated by comparing automatic ICs classifications with classifications performed by experienced investigators. Automatic and investigator classifications were highly consistent resulting in an overall accuracy greater than 99% in all datasets and decomposition levels, and an average sensitivity greater than 90%. Best results were attained when data were decomposed into a fewer number of components where the method achieved perfect sensitivity (100%). Performance was also evaluated by comparing automatic component classification with externally recorded ECG. Results showed that ICs automatically classified as artifactual were significantly correlated with ECG activity whereas the other ICs were not. We also assessed that the interference affecting EEG signals was reduced by more than 82% after automatic artifact removal. Overall, ARCI represents a significant step in the detection and removal of cardiac-related EEG artifacts and can be applied in a variety of acquisition settings making it ideal for sports science applications.

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“…Some epochs with significant movements or eye movements/blinks artefacts detected by fieldtrip 2 software were discarded. The heart beat artefacts are ignored in the research of the evoke response due to the less influence after averaging (Sun et al, 2016). The SEF was averaged across varying number of epochs from 20 to 199.…”

Section: Resultsmentioning

confidence: 99%

Noise cancellation for a whole-head magnetometer-based MEG system in hospital environment

Sun

Hämäläinen

Okada

2018

Biomed. Phys. Eng. Express

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We describe a strategy of removing magnetic field interference for a whole-head pediatric magnetoencephalography (MEG) system ("babyMEG") installed in a hospital. The 375-channel sensor array of babyMEG consists entirely of magnetometers in two layers to maximize the sensitivity for detecting MEG signals from infants, toddlers, and young children. It is equipped with a continuously operating closed-cycle helium recycler to reduce the operating costs. These two features pose special challenges for noise cancellation. Our strategy uses a combination of several methods. The system is installed in a light-weight, magnetically shielded room (MSR) equipped with an active external shielding. In addition we employ two software-based techniques - a signal space projection (SSP) technique and a synthetic gradiometer (SG) method - for removing the environmental magnetic noise in real time and displaying the output online. The shielding effects are: passive shielding - 36 dB, active shielding - 12 dB, SSP - 40 dB and SG - 40 dB, for a combined maximum shielding of about 90 dB at 0.1 Hz. We evaluated the performance of the babyMEG after applying the noise cancellation techniques. The dipole localization errors were <3 mm after averaging 50 epochs with empty room noise in a simulation study for dipoles >10 nAm, which is in the low range of empirically observed dipole moments. In a phantom study with realistic environmental noise, we could clearly recover an evoked cortical magnetic field produced by a 20 nAm dipole after averaging 50 epochs. The localization error was ~6 mm after averaging 20 epochs. In infants, we could clearly detect a somatic evoked field after averaging ~20 responses. The unique two-layer sensor design combined with the SSP or SG provides effective noise suppression for a magnetometer-based pediatric MEG system in hospital environment with the closed-cycle helium recycler operating continuously during MEG measurements.

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Removing Cardiac Artefacts in Magnetoencephalography with Resampled Moving Average Subtraction

Cited by 9 publications

References 35 publications

Recommendations and publication guidelines for studies using frequency domain and time‐frequency domain analyses of neural time series

Recommendations and publication guidelines for studies using frequency domain and time‐frequency domain analyses of neural time series

Automatic Removal of Cardiac Interference (ARCI): A New Approach for EEG Data

Noise cancellation for a whole-head magnetometer-based MEG system in hospital environment

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