Reporting standards

Objectives

• Report filters, reference, rejection, ICA, epoching, measurement and statistics to COBIDAS-MEEG standard
• Produce figures with uncertainty
• Share code and data (BIDS, environment files)

Why this matters

Everything the previous five lessons established is invisible in a paper unless the methods section says it. Whether the measurement window was fixed in advance, whether the spatial filter sat inside the cross-validation fold, whether the reference was chosen before the effect was plotted — a reader cannot infer any of it from the results, and the difference between an analysis with an error rate near α and one with an error rate near three in four lives entirely in those sentences. Reporting is not administration. It is the only channel through which the rigor of Level 6 reaches anybody.

Concepts

The checklist exists, and it is long for a reason

Community reporting standards for EEG and MEG — the COBIDAS-MEEG report ( (Pernet et al., 2020) ) and the earlier publication guidelines ( (Keil et al., 2014) ) — enumerate what a methods section must contain. They are long because the pipeline is long, and because almost every item on them is a value someone could otherwise have chosen after seeing the result.

Work through it by stage.

Participants and ethics. Number, age, sex or gender as recorded, handedness, relevant clinical characteristics and medication state, recruitment, exclusions with reasons and counts, compensation, ethics approval and consent. For patient studies, group-by-group, because pf-group-demographic-confound lives in the gap between “patients and controls were matched” and the actual numbers.

Acquisition. Amplifier class and bandwidth, cap and electrode type, electrode count and naming scheme, how positions were determined (template or digitized), online reference and ground, impedances and the criterion, sampling rate, online filters including any hardware notch, mains frequency, recording environment, and the session structure with block lengths. pf-hardware-bandwidth-ceiling and pf-notch-hole-in-band are both invisible unless the hardware filters are stated.

Events and timing. How triggers were generated, the measured stimulus-to-trigger latency and its jitter, and any correction applied. “Triggers were sent by the stimulus computer” does not tell a reader whether the latencies are comparable with anyone else’s (pf-trigger-offsets).

Preprocessing, in the order performed. Filter type (FIR or IIR), cutoffs, transition bandwidth or order, direction (zero-phase or causal), and what the filter was applied to — continuous data or epochs. Resampling rate and anti-alias handling. Bad-channel criterion, the channels flagged per subject, and whether they were interpolated and how. Reference, stated as what it is, plus the rank consequence of interpolation and average-referencing together (pf-interpolation-rank). ICA: algorithm, the data it was fitted on and any filter used only for the fit, the rank supplied, how components were classified, the criteria, and the number removed per subject with the range.

Epoching and rejection. Epoch window, baseline interval, rejection criteria as numbers, and the per-condition counts kept and lost. Per-condition, because pf-condition-biased-rejection is only visible in the comparison.

Measurement. Component, electrodes, window, measure, and — the item most often missing — where the window and electrodes came from. “From the prior literature”, “from a collapsed localizer”, “from the grand average of the condition difference” are three different analyses with three different error rates (L6.4).

Statistics. Unit of analysis; the model or test, written out; contrast coding; correction for multiple comparisons, over which family and by which procedure; for permutation tests the scheme, the count and the seed; α and tails; degrees-of-freedom method for any mixed model; effect size with a confidence interval; and the raw effect in µV. Non-significant results reported as well as significant ones.

Sharing. Data in BIDS where possible, with a data-availability statement and a licence; analysis code with a version-control reference; an environment file with pinned versions; seeds for anything random; and the derivatives needed to redraw the figures.

Figures that carry uncertainty

A grand-average difference wave drawn as a single line asserts a precision it does not have. Four habits fix most of it:

• Draw a band. A confidence interval or standard error around the grand average, and — for a within-subject effect — a within-subject-corrected interval, since the between-subject variance in overall amplitude is not part of the effect’s uncertainty. Say in the caption which interval it is.
• Show the subjects. Thin per-subject traces behind the average, or a per-subject scatter beside the bar, turns “the effect” into “the effect and how consistently it appeared”.
• Draw the analysis on the figure. Shade the measurement window, mark the electrodes, put the per-condition trial counts in the caption, and state the reference, the filter and the polarity convention. A figure that cannot be matched to the methods is a figure a reader has to trust.
• Share the scale. One colour scale in µV across topography panels, with the units on the bar; separate scales invite a comparison the data do not support.

And the negative rule: do not draw the significance and then describe it in words that the test does not support. A shaded “significant” region under a cluster test is a description of the cluster’s extent, and the caption has to say so (pf-cluster-inference-misread).

Reproducibility is a property of the archive, not the intention

Three things, in increasing order of how often they are forgotten:

1. Data. BIDS gives the directory layout, the sidecar metadata and the event tables a stranger needs; a data-availability statement gives the access route and the licence. Where the data cannot be shared, say why, and share the derivatives that can be.
2. Code. The analysis scripts, at the commit that produced the numbers, with the seed. A preregistration’s commit hash (L6.4) is what turns “written before” into a checkable claim.
3. Environment. Pinned package versions. MNE, EEGLAB, FieldTrip and every statistics package change behaviour between versions; “analysed in MNE-Python” dates a result without reproducing it.

A paragraph to audit

The paragraph below is a composite written for this lesson — not a quotation from any paper. It is deliberately the kind that passes review.

EEG was recorded from 64 scalp electrodes at 500 Hz using an active-electrode cap and a DC amplifier. Data were re-referenced to the average of all electrodes and band-pass filtered between 0.1 and 30 Hz. Bad channels were interpolated. Independent component analysis was used to remove ocular artifacts. Data were epoched from −200 to 800 ms relative to stimulus onset and baseline-corrected. Epochs containing artifacts were rejected. The P3 was quantified as the mean amplitude at Pz. Amplitudes were compared between conditions with a paired t-test, and effects were considered significant at p = .05 or below.

Read it once as a reviewer and once as someone trying to re-run it. The exercises below ask for both readings.

Explore

This lesson has no widget; the object of study is your own next methods section. Before the exercises, take a paper you have read recently — ideally one you found convincing — and try to rebuild its pipeline from the text alone, writing down each parameter you would have to guess. The count is usually between ten and twenty, and the exercise is more persuasive done on a paper you admire than on one you do not.

Practice

This lesson has no notebook. Its practice is the interactive checklist in the exercises below (spec §6 specifies a checklist component here in place of a notebook): work it against a real manuscript — your own, a preprint you are reviewing, or the paper you audit in C6 — and record the result. It is self-graded, because only you can see the manuscript; what it is for is to make the omissions visible while they can still be fixed.

Exercises

Exercise ex-6-6-methods-omissions

Multiple select

Read the composite methods paragraph above. Select every item below that the paragraph does NOT state.

Options (select all that apply)

The online reference used during recording.The filter type, its order or transition bandwidth, and whether it was applied causally or zero-phase.The epoch window relative to stimulus onset.The baseline interval.The electrode at which the P3 was measured.The measurement window over which mean amplitude was computed, and where that window came from.The artifact-rejection criterion and the number of epochs rejected in each condition.The sampling rate.The electrode type and amplifier class.

Check

Hint

Reporting checklist ex-6-6-cobidas-checklist

Checklist (self-graded)

Work this against a manuscript — yours, one you are reviewing, or the paper you audit in C6. Check an item only if the manuscript states it in a form a stranger could act on. Self-graded; nothing leaves your browser.

Checklist

Participants: number, age, sex or gender as recorded, handedness, clinical characteristics and medication state, per group.Exclusions: how many participants were excluded, by which criterion, and when it was fixed.Acquisition: amplifier class and bandwidth, cap and electrode type, electrode count and naming scheme.Electrode positions: template or digitized, and which template.Online reference and ground, and impedance criterion.Sampling rate, online filters including any hardware notch, and mains frequency.Events: how triggers were generated, the measured stimulus-to-trigger latency and its jitter, and any correction.Filters: type, cutoffs, transition bandwidth or order, direction, and whether applied to continuous or epoched data.Resampling: target rate and anti-alias handling.Bad channels: criterion, the count per subject, and whether and how they were interpolated.Reference used for analysis, and the rank consequence of interpolation plus average reference.ICA: algorithm, the data and filter it was fitted on, the rank supplied, classification criteria, components removed per subject.Epoching: window and baseline interval, both as numbers.Rejection: criteria as numbers, and epochs kept and lost per condition per subject.Measurement: component, electrodes, window, measure — and where the window and electrodes came from.Statistics: unit of analysis, the model or test written out, contrast coding, α and tails.Correction for multiple comparisons: the family, the procedure, and for permutation tests the scheme, the count and the seed.Effect size with a confidence interval, and the raw effect in µV; non-significant results reported too.Figures: uncertainty drawn, per-subject data visible, measurement window and electrodes marked, trial counts in the caption, shared colour scales with units.Data availability: BIDS where possible, access route, licence, and what is shared if the raw data are not.Code availability: scripts at the commit that produced the numbers, with seeds.Environment: pinned package versions for every analysis package used.Preregistration: a link and a commit hash, with every deviation reported as a deviation and the pre-registered analysis shown alongside.

0 / 23 checked. Self-graded.

Exercise ex-6-6-methods-audit

Free response

Audit the composite methods paragraph above in full. List what is missing, and for each omission say what a reader cannot check because of it — then write the two or three sentences you would add to fix the most serious ones.

Your answer

Reveal model answer

Pitfalls

No pitfall for this lesson: Spec §6 lists no pitfall for this lesson (§5.3 exemption).

In other tools

In other toolsEEGLAB · FieldTrip — names only

The equivalents of what this lesson does, for a reader who works in another toolbox. Function names only: their own documentation is the place to learn how to call them.

EEGLAB

• pop_exportbidsEEG-BIDS plugin

FieldTrip

• data2bidsFieldTrip
• ft_reproducescriptFieldTrip
• ft_analysispipelineFieldTrip

Names checked 2026-09-18 against EEGLAB 2026.0.0 (plugins at the versions in EEGLAB’s own plugin list) and FieldTrip 20251218.

Reading

1. Pernet et al. (2020). COBIDAS MEEG. unverified
2. Keil et al. (2014). Publication guidelines for EEG/MEG. unverified