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EEG and microstates

Checked 2026-07-23 against NeuroKit2 0.2.13 stable source/runtime, the official EEG/microstate APIs, and SPR EEG/MEG guidance.

Scope

NeuroKit2 does not expose an eeg_process() equivalent to its ECG/EDA pipelines. It provides selected feature, QC, re-reference, MNE, source, and microstate helpers. Use MNE or another validated EEG framework for the full acquisition/preprocessing workflow, while recording every transform and bad-segment decision.

For NumPy input, NeuroKit2 EEG functions expect shape:

(channels, time_samples)

Do not pass (time, channels) silently. Preserve channel names/order, montage, reference, sensor locations, units (typically volts in MNE), sampling rate, and bad channel/segment annotations.

Current stable helpers

Power

power = nk.eeg_power(
    eeg_channels_by_time,
    sampling_rate=250,
    frequency_band=["Gamma", "Beta", "Alpha", "Theta", "Delta"],
)

The argument is singular frequency_band, not frequency_bands. The pinned default array probe returned one row per channel with:

Channel, Gamma, Beta, Alpha, Theta, Delta

Standard named bands in the docs include Delta 1–4, Theta 4–8, Alpha 8–13, Beta 13–30, and Gamma 30–80 Hz, with additional sub-bands. Band definitions are conventions, not universal physiology. Report exact boundaries, PSD parameters, reference, artifact handling, absolute/relative normalization, and usable duration.

Bad channels

bads, channel_info = nk.eeg_badchannels(
    eeg_channels_by_time,
    bad_threshold=0.5,
    distance_threshold=0.99,
    show=False,
)

Return order is a list plus a DataFrame. The pinned info schema contained SD, Mean, MAD, Median, Skewness, Kurtosis, Amplitude, interval bounds, n_ZeroCrossings, and Bad.

This statistical screen is not a universal rejection rule. Review raw data, montage, bridging, line noise, drift, channel location, task, and condition. Fit thresholds without leaking group/condition outcomes.

Re-reference, GFP, and dissimilarity

rereferenced = nk.eeg_rereference(eeg_channels_by_time, reference="average")
gfp = nk.eeg_gfp(rereferenced, method="l1")
diss = nk.eeg_diss(rereferenced, gfp=gfp)

For array input eeg_rereference() returns an array. For MNE input it returns an MNE object. Average reference requires adequate channel coverage and bad-channel handling; it is not automatically appropriate for sparse montages.

eeg_gfp() defaults to L1 in NeuroKit2, while publications may use other definitions. Report method, standardization, normalization, smoothing, and reference.

Optional MNE requirements

Core NeuroKit2 does not install MNE. Stable functions that require it include:

Add only the optional package(s) required for the analysis at reviewed exact versions, commit/review the resulting uv.lock, and install with uv sync --locked. Do not install the upstream floating full extra in an automated workflow without such a lock.

Some MNE helpers download datasets/templates. Treat network access, cache paths, licenses, versions, and checksums as study dependencies; do not use them in a restricted/offline workflow without prior provisioning.

Source reconstruction

Stable signature:

eeg_source(raw, src, bem, method="sLORETA", show=False, ...)

It requires an MNE Raw object, source space, BEM/head model, montage/electrode locations, and appropriate co-registration. eeg_source_extract(stc, src, ...) returns region time series from a segmentation.

A template MRI does not validate localization for an individual or population. Report coordinate frames, digitization, head/conductivity model, inverse method, regularization, noise covariance, depth/orientation choices, atlas, and uncertainty. Do not use NeuroKit2 source estimates for diagnosis, surgical planning, or clinical localization.

Microstates

Segmentation

out = nk.microstates_segment(
    eeg_channels_by_time,
    n_microstates=4,
    train="gfp",
    method="kmod",
    sampling_rate=250,
    n_runs=50,
    random_state=42,
)

Stable methods include kmod, kmeans, kmedoids, pca, ica, and aahc. The pinned 0.2.13 output dict included:

Microstates, Sequence, GEV, GEV_per_microstate, GFP,
Polarity, Info, Info_algorithm

It did not use lowercase maps, labels, gfp, or gev. Microstates contains maps, and Sequence is the sample-wise class assignment.

Preparation and summaries

clean, train_indices, gfp, input_info = nk.microstates_clean(
    eeg_channels_by_time,
    sampling_rate=250,
    train="gfp",
)

static = nk.microstates_static(out["Sequence"], sampling_rate=250)
dynamic = nk.microstates_dynamic(out["Sequence"])

microstates_clean() is a utility for array normalization/standardization and training sample selection; it does not implement a full EEG artifact pipeline.

microstates_classify() is experimental and requires two arguments:

sequence, maps = nk.microstates_classify(
    out["Sequence"],
    out["Microstates"],
)

Its classification depends on channel ordering, so it is not a reliable substitute for template matching with a defined montage.

microstates_findnumber() returns (optimal_number, scores_dataframe). Choosing a state count from the same dataset and then testing selected-state effects can inflate research flexibility. Prespecify or cross-validate clustering choices and assess initialization stability.

Preprocessing/reporting

At minimum report:

Avoid frequency-band mental-state labels such as “beta = anxiety” or “theta/beta = ADHD.” EEG features are not diagnosis, consciousness monitoring, anesthesia control, seizure detection, or neurofeedback validation without a separate validated system and intended-use evidence.

Sources checked 2026-07-23