Mind the Noise Covariance When Localizing Brain Sources with M/EEG

Engemann, Denis A.; Strohmeier, Daniel; Larson, Eric; Gramfort, Alexandre

doi:10.1109/prni.2015.25

Cited by 11 publications

(5 citation statements)

References 13 publications

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“…For this we use the 30 ms to 300 ms window after the stimulus onset. The data covariance is again regularized automatically following (Engemann and Gramfort, 2015 ) and is motivated by the results from Woolrich et al ( 2011 ) and Engemann et al ( 2015 ).…”

Section: Source Reconstructionmentioning

confidence: 99%

A Reproducible MEG/EEG Group Study With the MNE Software: Recommendations, Quality Assessments, and Good Practices

et al. 2018

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Cognitive neuroscience questions are commonly tested with experiments that involve a cohort of subjects. The cohort can consist of a handful of subjects for small studies to hundreds or thousands of subjects in open datasets. While there exist various online resources to get started with the analysis of magnetoencephalography (MEG) or electroencephalography (EEG) data, such educational materials are usually restricted to the analysis of a single subject. This is in part because data from larger group studies are harder to share, but also analyses of such data often require subject-specific decisions which are hard to document. This work presents the results obtained by the reanalysis of an open dataset from Wakeman and Henson (2015) using the MNE software package. The analysis covers preprocessing steps, quality assurance steps, sensor space analysis of evoked responses, source localization, and statistics in both sensor and source space. Results with possible alternative strategies are presented and discussed at different stages such as the use of high-pass filtering versus baseline correction, tSSS vs. SSS, the use of a minimum norm inverse vs. LCMV beamformer, and the use of univariate or multivariate statistics. This aims to provide a comparative study of different stages of M/EEG analysis pipeline on the same dataset, with open access to all of the scripts necessary to reproduce this analysis.

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Section: Source Reconstructionmentioning

confidence: 99%

A Reproducible MEG/EEG Group Study With the MNE Software: Recommendations, Quality Assessments, and Good Practices

et al. 2018

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“…This dependence could also be altered when using inverse models other than beamforming. In fact, beamformers were found to be more strongly affected than minimum norm estimates by errors in covariance matrix estimation [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

Choice of Magnetometers and Gradiometers after Signal Space Separation

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López‐Sanz

Maestú

et al. 2017

Sensors

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Background: Modern Elekta Neuromag MEG devices include 102 sensor triplets containing one magnetometer and two planar gradiometers. The first processing step is often a signal space separation (SSS), which provides a powerful noise reduction. A question commonly raised by researchers and reviewers relates to which data should be employed in analyses: (1) magnetometers only, (2) gradiometers only, (3) magnetometers and gradiometers together. The MEG community is currently divided with regard to the proper answer. Methods: First, we provide theoretical evidence that both gradiometers and magnetometers result from the backprojection of the same SSS components. Then, we compare resting state and task-related sensor and source estimations from magnetometers and gradiometers in real MEG recordings before and after SSS. Results: SSS introduced a strong increase in the similarity between source time series derived from magnetometers and gradiometers (r2 = 0.3–0.8 before SSS and r2 > 0.80 after SSS). After SSS, resting state power spectrum and functional connectivity, as well as visual evoked responses, derived from both magnetometers and gradiometers were highly similar (Intraclass Correlation Coefficient > 0.8, r2 > 0.8). Conclusions: After SSS, magnetometer and gradiometer data are estimated from a single set of SSS components (usually ≤ 80). Equivalent results can be obtained with both sensor types in typical MEG experiments.

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“…Covariance matrices are typically estimated from the data, either from specific segments during a subject recording (e.g., the baseline period before each trial as a “noise covariance,” or during the trial for a “data covariance”) or from data recorded just before or after the experimental session (“empty-room” data). It is important that the true underlying sensor covariance structure is accurately reflected in the estimated noise covariances, otherwise a reduction in SNR and errors in source localization can be introduced ( 25 ). In the context of source localization of movement-compensated data in particular, it is also important that full (rather than diagonal) noise covariances are used ( 3 ).…”

Section: Signal Distortions and Their Correction In Infant Megmentioning

confidence: 99%

Improving Localization Accuracy of Neural Sources by Pre-processing: Demonstration With Infant MEG Data

et al. 2022

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We discuss specific challenges and solutions in infant MEG, which is one of the most technically challenging areas of MEG studies. Our results can be generalized to a variety of challenging scenarios for MEG data acquisition, including clinical settings. We cover a wide range of steps in pre-processing, including movement compensation, suppression of magnetic interference from sources inside and outside the magnetically shielded room, suppression of specific physiological artifact components such as cardiac artifacts. In the assessment of the outcome of the pre-processing algorithms, we focus on comparing signal representation before and after pre-processing and discuss the importance of the different components of the main processing steps. We discuss the importance of taking the noise covariance structure into account in inverse modeling and present the proper treatment of the noise covariance matrix to accurately reflect the processing that was applied to the data. Using example cases, we investigate the level of source localization error before and after processing. One of our main findings is that statistical metrics of source reconstruction may erroneously indicate that the results are reliable even in cases where the data are severely distorted by head movements. As a consequence, we stress the importance of proper signal processing in infant MEG.

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Mind the Noise Covariance When Localizing Brain Sources with M/EEG

Cited by 11 publications

References 13 publications

A Reproducible MEG/EEG Group Study With the MNE Software: Recommendations, Quality Assessments, and Good Practices

A Reproducible MEG/EEG Group Study With the MNE Software: Recommendations, Quality Assessments, and Good Practices

Choice of Magnetometers and Gradiometers after Signal Space Separation

Improving Localization Accuracy of Neural Sources by Pre-processing: Demonstration With Infant MEG Data

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