Notebooks

Electrodes, montages and the 10-20 system: standard montages, channel names and types, and where the reference is nb-0-2-montages

Downloads from ds-eegbci, ds-iowapd.

Used in L0.2 · Electrodes, montages and the 10-20 system

Amplifiers, sampling and recording: native formats, headers, events, timing, and a photodiode-measured trigger offset nb-0-3-first-load

Downloads from ds-eegbci, ds-lemon.

Used in L0.3 · Amplifiers, sampling and recording

Reading raw traces: the MNE raw browser, annotations and alpha reactivity nb-0-4-browse

Downloads from ds-eegbci.

Used in L0.4 · Reading raw traces

The artifact atlas: trace, spectrum and topography for each artifact type nb-0-5-artifact-atlas

Downloads from ds-eegbci.

Used in L0.5 · The artifact atlas

Data hygiene and metadata: the first-look checklist as code, an HTML report, and two planted problems nb-0-6-first-look-report

Downloads from ds-eegbci.

Used in L0.6 · Data hygiene and metadata

Sampling, Nyquist and aliasing: raw.resample() versus naive decimation on a 500 Hz recording nb-1-1-resampling

Downloads from ds-iowapd, ds-arithmetic.

Used in L1.1 · Sampling, Nyquist and aliasing

Time and frequency domains: the FFT of a real 2-s epoch, reconstruction from the top-k components, resolution versus epoch length nb-1-2-fft

Downloads from ds-eegbci.

Used in L1.2 · Time and frequency domains

Power spectral density: periodogram versus Welch, eyes open versus eyes closed, group averaging; windows, leakage and zero-padding nb-1-3-psd

Downloads from ds-eegbci.

Used in L1.3 · Power spectral density

Filters: FIR versus IIR, zero-phase versus causal, and four high-pass cutoffs on a P300 nb-1-5-filters

Downloads from ds-eegbci, ds-brain-invaders.

Used in L1.5 · Filters: FIR, IIR, and what they do to your data

Line noise: notch, spectral fit and a spatial (ZapLine-style) method at 60 Hz and 50 Hz, and a hardware notch hole nb-1-6-line-noise

Downloads from ds-iowapd, ds-eegbci, ds-dortmund, ds-arithmetic.

Used in L1.6 · Line noise

Aperiodic and periodic components: specparam fits, individual alpha frequency, exponent and offset across ten subjects nb-1-7-specparam

Downloads from ds-eegbci.

Used in L1.7 · Aperiodic and periodic components

Loading data and BIDS: read the file as it is stored, BIDS-ify a messy folder with mne-bids, round-trip it, and compare with the official OpenNeuro mirror nb-2-1-bids

Downloads from ds-eegbci.

Used in L2.1 · Loading data and BIDS

Channel locations and bad channels: pyprep NoisyChannels against a stated manual rule, interpolation, and what it costs the average reference and the rank nb-2-2-bad-channels

Downloads from ds-erpcore.

Used in L2.2 · Channel locations and bad channels

Re-referencing: the P3 at Pz under four references, what a reference does to a topography, and reconstructing an absent online reference channel nb-2-3-reference

Downloads from ds-erpcore, ds-lemon.

Used in L2.3 · Re-referencing

Filtering in practice: ERP distortion across six high-pass cutoffs, the two-pass ICA strategy measured, filtering epoched data, and a boundary artifact nb-2-4-filter-choices

Downloads from ds-erpcore.

Used in L2.4 · Filtering in practice

Artifact rejection strategies: a peak-to-peak threshold sweep, autoreject against fixed thresholds, and the per-condition rejection table nb-2-5-rejection

Downloads from ds-erpcore.

Used in L2.5 · Artifact rejection strategies

EOG regression and ICA: rank, the two-pass fit, ICLabel as a second opinion, before-and-after ERPs, and what over-cleaning costs nb-2-6-ica

Downloads from ds-erpcore.

Used in L2.6 · EOG regression and ICA

ASR, SSP and ICA on one recording: ERP signal-to-noise ratio, spectra, rank, and the ASR cutoff sweep nb-2-7-asr-vs-ica

Downloads from ds-erpcore.

Used in L2.7 · ASR, SSP and alternatives

Pipeline order and reproducibility: run_subject driven by one configuration file, the run log, and a per-subject QC report nb-2-8-pipeline

Downloads from ds-erpcore.

Used in L2.8 · Pipeline order and reproducibility

Epoching and baseline: Epochs with trial metadata on ERP CORE P3, and what the P3 does when the baseline window moves nb-3-1-epochs

Downloads from ds-erpcore.

Used in L3.1 · Epoching and baseline

The ERP and its components: the grand-average P3, target versus standard, over ten ERP CORE subjects, and the same component on 16 dry electrodes nb-3-2-erp-core-p3

Downloads from ds-erpcore, ds-brain-invaders.

Used in L3.2 · The ERP and its components

Measuring ERPs: peak, mean, area, peak latency and fractional-area latency compared for noise bias and across-subject reliability nb-3-3-measurement

Downloads from ds-erpcore.

Used in L3.3 · Measuring ERPs

Topographies and scalp maps: P3 map sequences under two references, map shape versus map amplitude, and where interpolation becomes extrapolation nb-3-4-topomaps

Downloads from ds-erpcore.

Used in L3.4 · Topographies and scalp maps

SNR, trial counts and design: standardized measurement error against trial count per subject, and the minimum trials for a stated threshold nb-3-5-sme

Downloads from ds-erpcore.

Used in L3.5 · SNR, trial counts and design

Single-trial approaches: an ERP image sorted by reaction time, single-trial P3 latency against reaction time, and a regression ERP nb-3-6-erp-image

Downloads from ds-erpcore.

Used in L3.6 · Single-trial approaches

Statistics for ERPs: a paired test on one measure, uncorrected time-point tests, and cluster-based permutation tests in time and over the scalp nb-3-7-cluster-test

Downloads from ds-erpcore.

Used in L3.7 · Statistics for ERPs

Why time-frequency: ERP against single-trial power on real motor-imagery trials, and the phase jitter at which a trial average halves nb-4-1-evoked-vs-induced

Downloads from ds-eegbci.

Used in L4.1 · Why time-frequency

STFT and Morlet wavelets: the wavelet's two full widths measured off MNE's own kernel, four cycle schemes on real epochs, and what padding cannot fix nb-4-2-wavelets

Downloads from ds-eegbci.

Used in L4.2 · STFT and Morlet wavelets

Multitaper and filter-Hilbert: three estimators on one epoch, calibrated to a common scale, and which one a 60-90 Hz analysis wants nb-4-3-multitaper-hilbert

Downloads from ds-eegbci.

Used in L4.3 · Multitaper and filter-Hilbert

Baseline normalisation and ERD/ERS: dB against percent against z, where the baseline goes, and the four averaging orders that give four different answers nb-4-4-erd

Downloads from ds-eegbci.

Used in L4.4 · Baseline normalization and ERD/ERS

Phase, ITC and cross-frequency coupling: what ITC is when there is nothing there, the P3 as a real positive control, the 40 Hz ASSR that is not one, and PAC with surrogates nb-4-5-itc

Downloads from ds-erpcore, ds-aszed, ds-eegbci.

Used in L4.5 · Phase, ITC and cross-frequency coupling

Is it really an oscillation? A spectral peak, bursts whose count falls elevenfold on the threshold alone, and cycle-by-cycle waveform shape nb-4-6-bursts

Downloads from ds-lemon.

Used in L4.6 · Is it really an oscillation?

Statistics for time-frequency: uncorrected cell counts, cluster-permutation and TFCE over frequency and time, and three analysis plans ranked by analyst degrees of freedom nb-4-7-tf-cluster

Downloads from ds-eegbci.

Used in L4.7 · Statistics for time-frequency

Volume conduction: the widget rebuilt in Python — why one source gives coherence exactly 1 between any two sensors, what sensor noise does to that, and the intracranial counterpoint nb-5-1-vc-sim

Downloads from ds-hup, ds-eegbci.

Used in L5.1 · Volume conduction: the central problem

Sensor-space connectivity: coherence, imaginary coherence, PLV, PLI and wPLI from one set of Fourier coefficients on ds-lemon eyes-closed, with a surrogate null, the reference dependence, and Granger causality on a simulation where the answer is known nb-5-2-connectivity

Downloads from ds-lemon.

Used in L5.2 · Sensor-space connectivity

Surface Laplacian and current source density: reference independence proved, the ERP CORE P3 sharpened, what depth and spatial extent cost under the Laplacian, and connectivity before and after CSD nb-5-3-csd

Downloads from ds-erpcore, ds-lemon.

Used in L5.3 · Surface Laplacian and CSD

Forward models without a boundary-element model: the leadfield on concentric spheres, one shell against four, what fewer electrodes cost, and template electrode positions against one subject's digitised set nb-5-4-forward

Downloads from ds-lemon.

Used in L5.4 · Forward models

The inverse problem on a sphere: the null space, minimum-norm, dSPM, sLORETA, eLORETA and an LCMV beamformer, resolution matrices, the depth bias — and why a deep source and a superficial patch cannot be told apart nb-5-5-inverse

Used in L5.5 · The inverse problem and source estimation

Source-space connectivity and leakage: leakage read off the resolution matrix, a simulation with one known coupling, pairwise and symmetric orthogonalisation, and the same measures on ds-lemon — on geometric regions, because no parcellation ships nb-5-6-source-connectivity

Downloads from ds-lemon.

Used in L5.6 · Source-space connectivity and leakage

Spatial filters for decoding: CSP plus LDA on ds-eegbci motor imagery with cross-validation, filters against patterns, the component-count sweep, the eleven points a misplaced fold boundary buys, and the twelve-subject distribution the headline number came from nb-5-7-csp

Downloads from ds-eegbci.

Used in L5.7 · Spatial filters for decoding

The multiple-comparisons landscape: one ERP contrast through six inference strategies, their error rates measured on a true null, and the family-wise error rate of the search itself nb-6-1-corrections

Downloads from ds-erpcore.

Used in L6.1 · The multiple-comparisons landscape

Mixed models and trial-level data: random intercepts, random slopes, crossed subject-by-item effects and single-trial regression on P3 amplitude, with the lme4 equivalents nb-6-2-lmm

Downloads from ds-erpcore.

Used in L6.2 · Mixed models and trial-level data

Effect sizes, power and precision: a simulated subjects-by-trials power surface for the P3, a between-group resting pilot on Iowa PD, and what a small pilot really tells you, measured by subsampling Dortmund nb-6-3-power-sim

Downloads from ds-erpcore, ds-iowapd, ds-dortmund.

Used in L6.3 · Effect sizes, power and precision

Circularity and analytic flexibility: the false-positive cost of choosing a window and electrode from the tested data, two fixes measured against it, and 486 defensible pipelines on one contrast nb-6-4-multiverse

Downloads from ds-erpcore.

Used in L6.4 · Circularity and analytic flexibility

Decoding as inference: time-resolved decoding of the P3 with a permutation test, chance and its variance, temporal generalization, and leakage measured three ways nb-6-5-decoding

Downloads from ds-erpcore, ds-eegbci.

Used in L6.5 · Decoding as inference

Decoding and BCI with moabb: CSP + LDA, a Riemannian tangent-space classifier and filter-bank CSP, benchmarked within session, across sessions and across subjects on motor imagery and on a dry-electrode P300, with session and EOG leakage measured nb-7-1-moabb

Downloads from ds-bci-iv-2a, ds-brain-invaders.

Used in L7.1 · Decoding and BCI

Sleep staging against an expert hypnogram: the irreversible Rechtschaffen & Kales to AASM mapping stated first, then Cohen's kappa over the whole recording and over the sleep period, with the confusion matrix and four sensitivity runs nb-7-2-sleep

Downloads from ds-sleep-edf.

Used in L7.2 · Sleep EEG and staging

A simulated real-time pipeline: the end-to-end latency budget derived, measured with a loopback probe and checked against the widget, then an alpha-envelope feedback loop with sham controls and the false-positive rate of the naive within-session test nb-7-3-realtime

Downloads from ds-eegbci.

Used in L7.3 · Real-time processing and neurofeedback

What survives at fourteen channels: three device classes measured with one estimator, the hardware bandwidth ceiling behind a nominal sampling rate, the vendor-derived columns that are not signal, and a controlled degradation of one recording from 64 electrodes to four nb-7-5-low-channel

Downloads from ds-eegbci, ds-brain-invaders, ds-areeg.

Used in L7.5 · Mobile and consumer EEG

How reliable is a resting-state feature? Split-half, minutes, two months and five years of test-retest ICC for the alpha peak, a theta/beta ratio and the aperiodic slope, against a clinical group contrast and the attenuation ceiling it implies nb-7-6-reliability

Downloads from ds-lemon, ds-dortmund, ds-srm, ds-iowapd.

Used in L7.6 · Resting-state and biomarkers

A pipeline you configure instead of write: mne-bids-pipeline on an ERP dataset and a resting one, the BIDS layout and event metadata a config cannot supply, the same subject through two pipelines, and the units error no configuration file could catch nb-7-8-bids-pipeline

Downloads from ds-erpcore, ds-srm.

Used in L7.8 · Reproducible pipelines at scale