3.1. MEEG device
MEEG studies should report basic information on the type of acquisition system being used (including manufacturer and model), the number of sensors and their spatial layout. For example, for EEG studies spatial layout will most likely correspond to the International 10-20 (Jasper, 1985; Klem et al., 1999), International 10-10 (Chatrian et al., 1985), International 10-5 (Oostenveld & Praamstra, 2001) or geodesic systems (Tucker, 1993). Additionally, the sensor material should be specified (e.g., Ag/AgCl electrodes) and whether the electrodes are active or passive.
For MEG studies, the type of sensors should also be specified (e.g., planar or axial gradiometers, or magnetometers; cryogenic or room-temperature), as well as the location and type of any reference sensors. Means of determining the position of the participant’s head with respect to the MEG sensor array should be reported, and also when this operation was performed (e.g., continuously, or at the start of each session). The type of shielded room (when used) should also be specified.
Additionally, for MEG studies, it is advisable to include “empty room” recordings using the same experimental set-up as during the experiment (but without the participant present) to characterize any participant-unrelated artifacts. For EEG studies, ideally access to the data in the calibration procedure (which has been carried out on the amplifiers prior to each recording session) would allow the variations in channel gains to be documented. In a similar fashion, it would be desirable to also be able to store/report electrode impedances that have been measured in each subject. This information would allow to compare raw effect sizes (in fT, uV) between studies and potentially harmonizing data across laboratories.
3.2. Acquisition parameters
For MEEG studies it is mandatory to specify basic parameters such as acquisition type (continuous, epoched), sampling rate and analogue filter bandwidth (including the parameters of the low pass anti-aliasing filter—an obligatory part of the recording system—as well as any high pass filtering). Notch filtering (to eliminate line noise), if used during recording, should also be reported. The inclusion of digitisation resolution (e.g., 16-bit or 24-bit) is also helpful. It should be noted that during data acquisition all MEEG recording systems will use some filter bandpass potentially as a default that may not be altered by the user. The inclusion of parameters related to filter type and roll-offs is essential in some situations (e.g., when discussing the timing of ERP components or spectral components). Note that the filter bandpass may also be adjusted post hoc for analysis, and this should also be reported when describing analysis procedures (see Section 4.3).
For EEG recordings, the location of reference and ground electrodes used in data acquisition should be specified. Similarly, reference electrode(s) used in data analysis should also be reported (see Section 4.4). For data acquisition, physically linked earlobe/mastoid electrodes should not be used, as they are not actually a neutral reference and make further modelling intractable (see also Katznelson, 1981). Further, distortions in EEG activity can occur as a result of relative differences in impedances between the two earlobe electrodes. While it has been recommended in various sources that the left earlobe/mastoid be used as acquisition reference, it should be noted that cardiac artifacts could be exaggerated if using a left earlobe/mastoid reference. An alternative would be to use the right earlobe instead.
Sensor position digitization procedures, if performed, should be described. For EEG, the type of approach used, and the manufacturer and model of the device should be specified, as well as the time in relation to the experiment that this procedure was performed. In MEG studies, when determining the position of the head with respect to the sensor array, the locations of EEG, other electrodes, or head localisation coils may be digitized at the same time. If high-resolution anatomical MRI scans of participants’ heads are acquired for the purposes of source localization, details of MRI scanning protocol, as well as fiducial types, their locations relative to anatomical landmarks, and the native coordinate system, should be described. If less commonly used fiducial positions are adopted, example photographs of fiducial placement might be helpful. Methods for co-registering MEEG sensors and fiducials to individual anatomical MRI scans or templates (including software name and version) should be reported (see also Sections 2.1 and 4.6).
Skin preparation methods used during electrode application, as well as the electrode material and the conducting gels or saline solutions (if used) should be described. The procedure used to measure impedances should be reported, especially for passive electrode systems. For systems using active electrodes it is not required nor always possible to record impedances, but nevertheless recommended if possible to report the impedance measurement procedure and values. Note that acceptable levels for electrode impedances vary relative to the ambient noise levels (e.g. whether recordings are done in a Faraday cage), the amplifier’s input impedance, and the type of electrodes being used (passive or active). Therefore it is advisable to include a statement on what the acceptable electrode impedances are for the specific setup (as suggested by the manufacturers), as well as what the actual values were (on average, or an upper bound). The time(s) at which impedances were measured during the course of the experiment e.g., start, middle, end, should also be noted. It is advisable to store the impedance measurements digitally, together with the EEG data, if at all possible.
Additional electrodes may be applied to the scalp/face to measure electro-oculographic (EOG) signals in either EEG or MEG studies. Additionally, EMG activity may be recorded from any part of the body. For EOG and EMG electrodes, their exact spatial locations should be specified, preferably relative to well-known anatomical landmarks (e.g., outer canthus of the eye). It should be specified if these data are collected with the same or different filter and gain settings to the MEEG data.
In MEEG recordings the position of the participant (e.g. sitting, lying supine) should be clearly documented. Head position is known to affect the strength of different EEG rhythms as it produces displacements of brain compartments and therefore has an appreciable effect on source modelling (Rice et al., 2013). This is likely to be an issue for MEG recordings also, as well as being an additional source of variance in comparison to fMRI data in the same participants where in one session the participant sits upright (in EEG or MEG) and in another (fMRI) the participant lies supine.
In some clinically based studies, some participants may be studied under sedation or anaesthesia. The anaesthetic agents may affect the MEEG data significantly, hence the agent, dosage and administration method (intravenous, intramuscular, etc) should be reported.
3.3. Stimulus presentation and recording of peripheral signals
Information on the type of stimulators (including manufacturer and model) should be provided (see Section 2). If being digitally controlled, the type and version of the software should also be reported. Calibration procedures for stimulators, if applicable, should be described. Similarly, manufacturer and model of devices used for collecting peripheral signals, such as a microphone to record speech output should be reported.
As MEEG methods have a very high temporal resolution, it is also essential to measure and report any time delays between stimulus timing or recording of peripheral signals with respect to the time course of the MEEG signals. For example, a visual or auditory stimulus setup may include a systematic delay from the trigger sent by the stimulus software to the actual arrival of the stimulus at the sensory organs. While a fixed delay is common and easy to fix a posteriori during analysis, randomness in temporal jitter can be highly problematic. Any information that may influence the interpretation of the results, such as stimulus strength or timing, visual angle, microphone placement etc should be reported. For studies involving hyperscanning, a description of the synchronization of multiple data acquisition systems (e.g. EEG-EEG, MEG-EEG, EEG-fMRI) should be provided.
3.4. Vendor specific information
When providing acquisition information in a manuscript keep in mind that readers may use a different manufacturer of EEG or MEG device, and thus one should minimize the use of vendor-specific terminology. To provide comprehensive acquisition detail we recommend reporting vendor-specific information in particular regarding hardware parameters, but with generic and agreed terminology (see e.g. the brain imaging data structure, or BIDS). If space constraints are a problem in manuscript preparation, these details could be provided as supplementary material.
4. Preprocessing and processing reporting
4.1. Software-related issues
Many of the available EEG and MEG systems come with analysis software packages with varying levels of detailed descriptions of how the different preprocessing tools are implemented. In addition, several freely available software packages that run on MATLAB/Python/R platforms, or commercial data analysis packages offer alternative implementations of data analysis tools. In addition, custom-written software can be used. The software that has been used for the preprocessing and subsequent analysis must be indicated (including the version). In-house software should be described in explicit detail with reference to the peer-reviewed or pre-print materials. The source code should be publicly released and access links should be provided (e.g., GitHub or another readily accessible internet-based location).
4.2. Defining workflows
Preprocessing is a crucial step in MEEG signal analysis as data are typically distorted due to various factors. The sequence of steps in the preprocessing pipeline and their order influences the data to be used for subsequent analysis. The workflow, therefore, has to be described step-by-step and with such a level of detail that it could be exactly reproduced by another researcher. For most studies, recommended steps after general visual data inspection include: 1) Identification and removal of electrodes/sensors with poor signal quality i.e. identification of bad channels. It is essential to clearly describe the methodology and the criteria used, particularly if interpolation is used. 2) Artifact identification and removal. State the method and criteria used to identify artifacts. If a tool is used to automate this step, details on its implementation and parameters used should be provided. 3) Detrending (when and if appropriate). 4) Digital low- and high-pass filtering with filter-type characteristics (IIR/FIR, type of filter [e.g., Butterworth, Chebyshev etc], cut-off frequency, roll-off/order, causal, zero-phase etc.). 5) Data segmentation (if performed). 6) Additional identification/elimination of physiological artifacts (blinks, cardiac activity etc). 7) Baseline correction (when and if appropriate). 8) Re-referencing for EEG (e.g., earlobe/mastoid-reference, common-average reference, bipolar) and expression of the data in another form (e.g. surface Laplacian; when and if desired).
The steps and sequence described above are appropriate for most basic analyses of data. That said, for specific analyses, or due to specific data characteristics, the order of processing may vary for scientific reasons. For example, data segmentation could occur at different points in the pipeline, depending in part on the specific artifact removal methods used. Note, however, that filtering should be performed before data segmentation to avoid edge effects, or alternatively sufficient data padding should be used. Data re-referencing could also theoretically be performed at various points in the pipeline, but it is important to note that re-referencing can lead to a spatial spread of artifacts. The committee recognizes that investigators require a pipeline where the order of steps is taken for specific reasons, and hence we are not prescriptive about a particular order of data analysis. That said, for each study, the order of the steps in the preprocessing pipeline should be motivated and made explicit, so that other investigators can replicate the study.
Visual inspection of the spatiotemporal structure in the signals after each step is recommended and, if needed, remaining segments of poor data quality should be marked and excluded from further analysis. When such epochs are additionally rejected, a record should be provided such that the same analysis could be reproduced from the raw data. Ideally, storing it in samples relative to the onset of the data record, would be desirable to avoid the potential ambiguity which can arise when reporting more or less arbitrary ordinal epoch numbers. During preprocessing, topographic maps of the distribution of the means and variances of scalp voltages (for EEG) and magnetic fields (for MEG) can serve as an additional tool for spotting channels with poor data quality that might escape detection in waveform displays (Michel et al., 2009).
4.3. Artifacts and filtering
Artifacts from many different sources can contaminate MEEG data and must be identified and/or removed. Artifacts can be of non-physiological (bad electrode contact, power line noise, flat MEG or EEG channel etc.) or physiological (pulse, muscle activity, sweating, movement, ocular blinks etc.) origin. The data should first be visually inspected to assess what types of artifact are actually present in the data. This evaluation should not be biased by the knowledge of the experimental conditions. Subsequently, established artifact identification/removal pipelines can be run, or an alternative motivated cleaning procedure can be implemented. Artifacts can be dealt with in different ways, from simply removing artifact-contaminated segments or channels from the data, to separating signal from noise using e.g. linear projection/spatial filtering techniques.
If automatic artifact detection methods are used, they should be followed up by visual inspection of the data. Any operations performed on the data (see Section 4.1 workflow) should therefore be described, specifying the parameters of the algorithm used. It is recommended to describe in detail the type of detrending performed and the algorithm order (e.g., linear 1st order, piecewise, etc). When automatic artifact rejection/correction is performed, which method was used and what was the range of parameters (e.g., EEG data with a range larger than 75 microV, epoch rejected based on 3 standard deviations from the mean kurtosis). Similarly for channel interpolation, it is essential to specify the interpolation method and additional parameters (e.g., trilinear, spline order). For example, when independent component analysis (ICA, Brown et al., 2001, Jung et al., 2001, Onton et al., 2006) is used, describe the algorithm and parameters used, including the number of ICs that were obtained. If artifacts are rejected using ICA or other signal space separation methods, it is important to report how these were identified if one wants to reproduce the method used. It is worthwhile to also consider including topographies of components in the Supplementary Materials section of manuscripts (when available). If interactive artifact rejection procedures are used, it is essential to describe what types of features in the MEEG signal were identified and define the criteria used to reject segments of data. This also allows the reader to reproduce the results, as well as to be able to compare results between studies (see above on reporting visually removed trials, or epochs, for instance). Once artifacts have been removed, the average number of remaining trials per condition should be reported.
In addition to removing artifact-contaminated segments or using ICA as a popular linear projection technique, MEG allows for the application of specialized linear projection techniques, which in some situations can be used in isolation. For example, signal-space projection methods (SSP, Uusitalo & Ilmoniemi, 1997) use “empty room” measurements to estimate the topographic properties of the sensor noise and project it out from recordings containing brain activity. Related tools with a similar purpose include signal space separation (SSS) methods and their temporally extended variants (tSSS, Taulu et al., 2004; Taulu & Simola, 2006) that rely on the geometric separation of brain activity from noise signals in MEG data. SSS methods have been recommended as being superior to SSP (Haumann et al., 2016). The ordering of preprocessing steps for cleaning MEG data is particularly important, due to potential data transformation – for some caveats see Gross et al., 2013.
For both MEG and EEG data, particular attention must be taken to describe temporal filtering, both for data acquisition and post-processing, as this can have dramatic consequences on estimating time-courses and phases (Rousselet, 2012; Widmann & Schröger, 2012). Some investigators have advocated the use of a sampling rate that is 4 times above the intended cut-off frequency of the low pass filter used (Luck et al., 2014 and latest IFCN guidelines). That said, the roll-off rate/slope of the filter should also be taken into consideration, because there will still be some signal that is present above the filter cut-off frequency. Therefore specifying with a description of the type, bands etc. of the filters used is useful. The type and parameters of any applied post-hoc filter and (for EEG, EOG and EMG) re-computed references for EEG has to be specified, as they can crucially affect the outputs of waveform or frequency analyses (but not topographies). For frequency and time-frequency analyses, details on the transformation algorithm should similarly be reported.
Data preprocessing also forms an essential part of multivariate techniques, and can dramatically affect decoding performance (Guggenmos et al., 2018). We recommend to carefully describe the method used, in particular, if noise normalization is performed channel wise (univariate normalization) or for all channels together (multivariate normalization, or whitening). For the latter, the covariance estimation procedure must be specified (based on baseline, epochs, or for each time point) as its strong impact on results (Engemann & Gramfort, 2015) can hinder any attempt to reproduce the analyses.
4.4. Re-referencing
EEG is a differential measure and in non-clinical EEG is usually recorded relative to a fixed reference (in contrast to clinical practice, which usually uses bipolar montages). While EEG is recorded relative to some reference, it can later be re-referenced by subtracting the values of another channel or weighted sum of channels from all channels. The need for re-referencing depends on the goals of the analysis and EEG measures used (e.g., common average reference, see below) and can be beneficial for evaluation of connectivity and for source modelling. However, note that, independently of the actual re-referencing scheme, sensor level interpretation of connectivity is invariably confounded by spatial leakage of source signals (Schoffelen & Gross, 2009). Re-referencing does not change the contours of the overall scalp topography since relative amplitude differences are maintained. This can, however, cause issues when working on single channels or clusters, because amplitudes do change locally with referencing (Hari & Puce, 2017). Specifically, the shape of the recorded waveforms at specific electrodes can be altered and this will also affect the degree of distortion of waveforms by artifacts. Hence, when comparing across experiments, the references used should be taken into account, and if unusual, the reference choice should be justified. For EEG, the channel(s) or method used for re-referencing must be specified. MEG is essentially reference free, but some systems may allow for ‘re-referencing’ of the signals recorded close to the brain, using signals recorded at a set of reference coils far away from the brain. If these types of balancing techniques are used, they should be adequately described.
Re-referencing relative to the average of all channels (common average reference, CAR) is most common for high-density recordings as the first step in current practice. The main assumption behind the CAR is that the summed potentials from electrodes spaced evenly across the entire head should be zero (Bertrand et al., 1985, Yao, 2017). Although it is generally admitted that this is a good approximation for EEG data sets of 128 sensors or more (Srinivasan et al., 1998; Nunez & Srinivasan, 2006), the effect of re-referencing to a CAR has been found to be of no close relation to the electrode density. The sum of potential is mainly affected by the coverage area and the neural source activating orientation (Hu et al., 2018a). For low density recordings and ROI-based analyses in sensor space there is a serious risk of violating the assumptions for the average reference and the possibility of introducing shifts in potentials (Hari & Puce, 2017) and thus CAR should be avoided in low-density recordings (<128 channels).
An alternative to the CAR approach is the ‘infinite reference’ one, also known as Reference Electrode Standardization Technique (REST and regularized REST) (Yao, 2001). Both the CAR and REST have been shown to be the extremes of a family of Bayesian reference estimators (Hu et al., 2018b). REST utilizes the prior that EEG signals are correlated across electrodes due to volume conduction, while CAR takes the prior that EEG signals are independent over electrodes (for reviews see Yao et al., 2019; Hu et al., 2019). If the focus of the data analysis is on source space inference (see Section 4.6), re-referencing is, in theory, not necessary but may be useful for comparisons to existing literature. Of note, any linear transform applied to the data (e.g. CAR) should also be applied to the forward matrix used for source space analysis. Such important details are generally taken care of by software tools in the field (and some require data to be in CAR form), but it is worthwhile ensuring that this is done. Finally, it should also be noted that there are so-called ‘reference-free’ methods, the most common one being the current source density (CSD) transformation, that usually relies on the spatial Laplacian of the scalp potential i.e. the second spatial derivative of the scalp voltage topography (Tenke & Kayser, 2005). Such techniques attempt to compensate, in EEG, for the signal smoothing due to the low electrical conductivity of the scalp and skull. When this is used, the software and parameter settings (interpolation method at the channel level and algorithm of the transform) must be specified.
4.5. Spectral and time-frequency analysis
A common approach for the analysis of MEEG data is to examine the data in terms of its frequency content, and these analyses are applicable for both task-related as well as resting state designs. One important caveat for these types of analyses is that the highest frequencies that could occur in the data be first considered. The selected data acquisition rate must be at least 2 times (Nyquist theorem) the highest frequency in the data, but is often higher because of the filter roll-off (see Section 4.3) – underscoring the importance of planning all data analyses prior to data acquisition, ideally during the design of the study.
In task-related designs, MEEG activity can be classified as evoked (i.e., be phase-locked to task events/stimulus presentation) or induced (i.e., related to the event, but not exactly phase-locked to it). Hence, it is important to specify what type of activity is being studied. The domain in which the analysis proceeds (time and frequency or frequency alone) should be specified, as should the spectral decomposition method used (see below), and whether the data are expressed in sensor or source space. These methods can be the precursor to the assessment of functional connectivity (see Section 4.6).
The spectral decomposition algorithm, as well as parameters used, should be specified in sufficient detail since these crucially affect the outcome. Therefore, depending on the decomposition method used (e.g., wavelet convolution, Fourier decomposition, Hilbert transformation of bandpass-filtered signals, or parametric spectral estimation), one should describe the type of wavelet (including the tuning parameters), the exact frequency or time-frequency parameters (frequency and time resolutions), exact frequency bands, number of data points, zero padding, windowing (e.g., a Hann or Hanning window), and spectral smoothing (Cohen, 2018). It is relevant to note that the required frequency resolution is defined as the minimum frequency interval that two distinct underlying oscillatory components need to have in order to be dissociated in the analysis (Bloomfield, 2004; Boashash, 2003). This should not be mistaken with the increments at which the frequency values are reported (e.g., when smoothing or oversampling is used in the analyses). When using overlapping windows (e.g., in Welch’s method) or using Multi-taper windows for robust estimation, the potential spectral smoothing may lead to closely spaced narrow frequency bands to blend. This should be carefully considered and reported.
4.6. Source modelling
MEEG data are recorded from outside the head. Source modelling is an attempt to explain the spatio-temporal pattern of the recorded data in sensor space as resulting from the activity of specific neural sources within the brain (in source space), a process known as solving the inverse problem. Since there is no unique solution to the inverse problem, (i.e. it is mathematically ill-posed), additional assumptions are needed to constrain the solution. Source modelling requires a forward model, which models the sensor level distribution of the EEG potential or MEG magnetic field for a (set of) known source(s), modelling the effect of the tissues in the head on the propagation of activity to MEEG sensors. Forward and inverse modelling require a volume conduction model of the head and a source model, both of which can crucially influence the accuracy and reliability of the results (Baillet et al., 2001; Michel & He, 2018). Practically, the forward model (or lead field matrix) describes the magnetic field or potential distributions in sensor space that result from a predefined set of (unit amplitude) sources. The sources are typically defined either in a volumetric grid, or on a cortically constrained sheet. Information from the forward model is then used to estimate the solution of the inverse problem, in which the measured MEEG signals are attributed to active sources within the brain. It is important to note that source modelling procedures essentially provide approximations of the inverse solution as solved under very specific assumptions or constraints.
In addition to the MEEG data itself, forward and inverse modelling requires a specification of the spatial locations of the sensors relative to the head (Section 3.2), a specification of the candidate source locations, the source model, and geometric data that is used as a volume conduction model of the head, e.g., a spherical head model, or a more anatomically realistic model, based on an individual anatomical MRI of the entire head (i.e. including the scalp and face). Note that this may have implications for subject privacy when sharing data (see Section 7.2). The procedure used to coregister the locations of measurement sensors and fiducials with geometric data must be described (see Section 2.1 for definitions; Section 3.2 for sensor digitization methods). If using anatomical MRI data, it should be made clear if a normalized anatomical MRI volume such as the MNI152 template, or individual participant MRIs have been used for data analysis. If individual MRIs have been used, the data acquisition parameters should be described.
It is essential that all details of the head model and the source model are given. The numerical method used for the forward model (e.g., boundary element modelling (BEM), finite element modelling (FEM)) must be reported, and the values of electrical conductivity of the different tissues that were used in the calculations must be specified. This is less of a problem for MEG where magnetic fields are not greatly distorted by passing through different tissue types (Baillet, 2017). The procedure for the segmentation of the anatomical MRI into the different tissue types should be described. For the source model, the number of dipole locations should be reported, as well as their average positions. Moreover, it should be specified how the source model was constructed, whether it describes a volumetric 3D-grid, or a cortically constrained mesh. . When using cortically constrained (surface-based or volumetric) source models, these should ideally be based on an individual MRI of the participant’s head, especially in clinical studies where brain lesions or malformations may be involved. That said, it has been argued that in certain clinical settings, approximate head models might be adequate, although their limitations should be explicitly acknowledged (Valdés-Hernández et al., 2009). The source localization method (e.g., equivalent current dipole fitting, distributed model, dipole scanning), software and its version (e.g., BESA, Brainstorm (Tadel et al., 2011), Fieldtrip (Oostenveld et al., 2011), EEGLAB (Delorme & Makeig, 2004), LORETA, MNE (Gramfort et al., 2013), Nutmeg (Dalal et al., 2004), SPM (Litvak et al., 2011), etc.) must be reported, with inclusion of parameters used (e.g., the regularization parameter) and appropriate reference to the technical paper describing the method in detail. Finally, it should be noted that the original mixing from the neural sources to the scalp/sensors signals cannot be completely undone with even perfect source reconstruction, and this is specifically an important confounder for connectivity analyses (Schoffelen & Gross, 2009, Palva et al., 2018, Pascual-Marqui et al., 2018)
4.7. Connectivity analysis
We refer here to connectivity analyses as any method that aims to detect functional coupling between two or more channels or sources. It is important to report and justify the use of either sensor or source space for the calculation of derived metrics of coupling (e.g., network measures such as centrality or complexity). Particular attention should be given to the anatomical parcellation scheme, explaining how this was performed (see e.g. Douw et al., 2017). Recent results have shown strong differences for connectivity computed in subject spaces vs. template space ( Farahibozorg et al., 2018, Mahjoory et al., 2017) Recent general references on connectivity measures have been published (Bastos & Schoffelen, 2016; O’Neill et al., 2018; He et al., 2019).
When using multivariate measures (either data-driven or model-based) such as partial coherence and multiple coherence, the exact variables that have been analysed and the exact variables with respect to which the data was partialised, marginalised, or conditioned should be described. When reporting measures of data-driven spectral coherence or synchrony (Halliday et al., 1995) the following aspects should be considered and reported: the exact formulation (or reference), whether the measure has been debiased, any subtraction or normalisation with respect to an experimental condition or a mathematical criterion. In case of Auto-Regressive (AR)-based multivariate modelling (e.g., in the Partial Directed Coherence group of measures; Baccala & Sameshima, 2001), the exact model parameters (number of variables, data points and window lengths, as well as the estimation methods and fitting criteria) should be reported. Finally, epoch length must be reported as it influences greatly connectivity values especially considering sensor vs source space (Fraschini et al., 2016).
While the committee agrees that statistical metrics of dependency can be obtained at the channel level, it should be clear that these are not per se measures of neural connectivity (Haufe et al., 2012). The latter can only be obtained by an inferential process that compensates for volume conduction and spurious connections due to unobserved common sources or cascade effects. In spite of that, dependency measures can be useful for e.g., biomarking. The possible insight into brain function derived from these measures should be critically discussed. This is particularly important since the interpretation of MEEG-based connectivity metrics may be confounded by aspects of the data that do not directly reflect true neural events (Schoffelen & Gross J, 2009; Valdes Sosa et al., 2011). Inference about connectivity between neural masses can only be performed with dependency measures at the source level and correct inferential procedures. For potential issues in dealing with connectivity analyses across channels versus sources, see Lai et al., 2018.
Table 2. Data Acquisition check-sheet
| Device |
– MEG or EEG manufacturer, model, sensor specifications
– Details on additional devices used (manufacturer and make) for additional measures (behaviour or other) |
| Sensor type and spatial layout |
– MEG: planar/axial gradiometers and/or magnetometers, and their number and locations
– Electrodes for EEG, EOG, ECG, EMG, skin conductance (electrode material, passive/active, other)
– EEG spatial layout: 10-20, 10-10 system, Geodesic, other. Document number of electrodes. If layout is not conventional, show a 2D map of electrode positions |
| Participant preparation and test room |
– Ambient characteristics and lighting (and if appropriate, empty room recording for MEG), detail if the recording room was shielded for EEG
– Participant preparation (EEG: skin preparation prior to electrode application, electrode application; MEG: participant degaussing, special clothing) |
| Impedance measurement |
– Report impedances for EEG/EOG/ECG/EMG electrodes, preferably digitally storing impedance values to the datafile, indicate timing of impedance measurement(s) relative to the experiment |
| Data acquisition parameters |
– Software system used for acquisition
– Low- and high-pass filter characteristics and sampling frequency
– Continuous versus epoched acquisition?
– For EEG/EOG/ECG/EMG/skin conductance: report reference and ground electrode positions |
| Sensor position digitization |
– EEG/EOG: method (magnetic, optical, other), manufacturer and model of the device used
– MEG: monitoring of head position relative to the sensor array, the use of head movement detection coils and their placement
– In both MEG and EEG, report the time of digitization in relation to the experiment, and describe the 3D coordinate system |
| Synchronization of stimulation devices with MEG and/or EEG amplifiers |
– Report either accuracy or error in synchronization
– Synchronization between hyperscanning MEG or EEG amplifiers / MRI clock and EEG amplifiers |
