GUI Tutorials#

EEGPrep’s GUI is built for practical EEG review: use menus and dialogs for visual decisions, keep eegprep-console beside it for inspection and repeatable commands, and save the resulting datasets when review is complete.

Launch#

Use the full menu set while learning:

uv run eegprep-console --full

Use the GUI-only launcher when you do not need a console:

uv run eegprep-gui --full

The console workflow is recommended because it shows LASTCOM and ALLCOM after each menu action. See GUI and Console Together for the shared-session model and implementation details.

Load, Inspect, and Save a Dataset#

Open File > Load existing dataset.

Select sample_data/eeglab_data.set.

In the console, run EEG["data"].shape, len(ALLEEG), CURRENTSET, and LASTCOM.

Choose Plot > Channel data (scroll) to inspect the current dataset in EEGBrowser.

Choose File > Save current dataset as when you want a new .set file.

Equivalent console calls:

from pathlib import Path
from eegprep import pop_loadset, pop_saveset

EEG = pop_loadset(Path("sample_data") / "eeglab_data.set")
pop_saveset(EEG, Path("sample_data") / "eeglab_data_reviewed.set")

Filter and Resample#

Use these steps for a small, inspectable preprocessing pass on the tutorial dataset:

GUI action	Console equivalent
`Tools > Filter the data`	`pop_eegfiltnew(EEG, locutoff=1, hicutoff=40)`
`Tools > Change sampling rate`	`pop_resample(EEG, 64)`
`Plot > Channel spectra and maps`	`pop_spectopo(EEG, 1, [])`

Filtering with pop_eegfiltnew uses the bundled FIRFilt-style Hamming-window FIR defaults. Boundary events are respected when continuous data contains breaks. Resampling updates event latencies and clears stale ICA activations.

Clean Continuous Data#

The bundled clean_rawdata workflow is available through the extension menu surface and from Python:

from eegprep import pop_clean_rawdata

EEG, com = pop_clean_rawdata(
    EEG,
    FlatlineCriterion=5,
    ChannelCriterion=0.8,
    LineNoiseCriterion=4,
    Highpass=(0.25, 0.75),
    BurstCriterion=20,
    WindowCriterion=0.25,
    return_com=True,
)

Run this on continuous data before epoching. If the dialog offers a rejected data browser, accepting the browser keeps marks and history synchronized with the shared session.

Run ICA and Review ICLabel#

For a fast review tutorial, load the ICA sample first:

EEG = pop_loadset("sample_data/eeglab_data_epochs_ica.set")

Then use the GUI:

Choose Tools > Decompose data by ICA for datasets that do not already contain ICA fields. The sample above already has ICA.

Choose Tools > Classify components using ICLabel.

Choose Plot > Component properties to open the ICLabel-aware property dashboard.

Toggle component rejection marks, press OK, and inspect EEG["reject"]["gcompreject"] in the console.

Choose Tools > Remove components from data only after reviewing labels, scalp maps, spectra, activity, and any DIPFIT fields.

Equivalent console calls:

from eegprep import eeg_icalabelstat, pop_icflag, pop_iclabel, pop_subcomp, pop_viewprops

EEG, com = pop_iclabel(EEG, "default", return_com=True)
stats = eeg_icalabelstat(EEG, threshold=0.9, verbose=False)
figures = pop_viewprops(EEG, typecomp=0, chanorcomp=[1, 2, 3])
EEG, com = pop_icflag(EEG, return_com=True)
EEG, com = pop_subcomp(EEG, [], return_com=True)

Use the empty component list in pop_subcomp(EEG, []) to remove components currently marked in EEG.reject.gcompreject.

Use EEGBrowser for Rejection#

Plot > Channel data (scroll) opens EEGBrowser for inspection. Rejection workflows open the same browser in a marking mode. Continuous marks can become sample-removal intervals through eeg_eegrej; epoch marks can update EEG.reject.rejmanual and then be removed with pop_rejepoch.

See EEGBrowser Workflows for browser modes, mark fields, and performance behavior.

Create a STUDY#

With multiple datasets loaded in ALLEEG:

Choose File > Create study > Using all loaded datasets.

Inspect STUDY and CURRENTSTUDY in the console.

Choose Study > Precompute channel measures for ERP, spectrum, ERSP, or ITC caches.

Choose Study > Plot channel measures to review cached measures.

Choose File > Save current study as for a durable .study file.

Equivalent console call:

STUDY, ALLEEG, com = pop_study(None, ALLEEG, name="Tutorial study", return_com=True)

See STUDY Workflows for design variables, cached measures, clustering, PAC, and explicit optional-backend boundaries.

DIPFIT Source Workflow#

Dataset-level DIPFIT helpers are bundled for standalone spherical workflows. Use them after ICA:

from eegprep import pop_dipfit_gridsearch, pop_dipfit_nonlinear, pop_dipfit_settings, pop_dipplot

EEG, com = pop_dipfit_settings(EEG, model="standardBESA", return_com=True)
EEG, com = pop_dipfit_gridsearch(
    EEG,
    [1],
    [-40, -20, 0, 20, 40],
    [-40, -20, 0, 20, 40],
    [20, 40, 60],
    40,
    return_com=True,
)
EEG, com = pop_dipfit_nonlinear(EEG, component=1, return_com=True)
figures, com = pop_dipplot(EEG, [1], summary="on", return_com=True)

MRI-derived BEM creation, AFNI atlas clipping, LORETA analysis, and STUDY-level source statistics remain explicit backend boundaries. EEGPrep raises clear errors for unavailable external workflows instead of producing placeholder results.

Troubleshooting GUI State#

If a menu item is disabled, check the current state:

CURRENTSET
CURRENTSTUDY
EEG.get("icaweights")
EEG.get("trials")

Use --full to show legacy and advanced menu entries. Use --no-plugins to diagnose extension startup problems with only core EEGPrep menus.