Recommended Offline EEG Denoising Pipeline

This tutorial provides a practical, end-to-end offline denoising workflow using GEDAI on EEG data.

The pipeline is intentionally simple:

1. Apply a broadband Gedai model to remove large artifacts.

2. Apply AdaptiveMultibandGedai for frequency-specific refinement.

Use this tutorial as a template and adapt only the data-loading block and parameter values for your own dataset.

from mne.io import read_raw

from gedai import AdaptiveMultibandGedai, Gedai
from gedai.data import get_contaminated_eeg_set_path
from gedai.viz import plot_mne_style_overlay_interactive

n_jobs = -1

raw = read_raw(str(get_contaminated_eeg_set_path()), preload=True)

Preprocessing GEDAI will automatically apply an average reference before fitting or transforming the data. If your acquisition used a different reference, consider adding the missing reference channel beforehand to preserve the rank of your data. For example, if your data was recorded with a Cz reference, you can add a virtual Cz channel as follows: raw.add_reference_channels("Cz", copy=False).

High-pass filtering before GEDAI usually improves covariance estimation by reducing slow drifts and non-stationarities.

raw.filter(l_freq=0.5, h_freq=None, n_jobs=n_jobs)

	General
	Filename(s)	SNR=0.35481 contamination=25 clean_EEG_dataset_2.set + EOG_EMG_NOISE_dataset_1.set
	MNE object type	RawEEGLAB
	Measurement date	Unknown
	Participant	Unknown
	Experimenter	Unknown
	Acquisition
	Duration	00:01:00 (HH:MM:SS)
	Sampling frequency	200.00 Hz
	Time points	12,000
	Channels
	EEG	27
	Head & sensor digitization	30 points
	Filters
	Highpass	0.50 Hz
	Lowpass	100.00 Hz

Broadband GEDAI

broadband_gedai = Gedai()
broadband_gedai.fit_raw(raw, noise_multiplier=6.0, n_jobs=n_jobs)
broadband_denoised_raw = broadband_gedai.transform_raw(
    raw, n_jobs=n_jobs, verbose=False
)

Adaptive Multiband GEDAI

adaptive_multiband_gedai = AdaptiveMultibandGedai(
    wavelet_type="haar", wavelet_level=5, cycles_per_wavelet=10
)
adaptive_multiband_gedai.fit_raw(
    broadband_denoised_raw, noise_multiplier=3.0, n_jobs=n_jobs
)
adaptive_multiband_denoised_raw = adaptive_multiband_gedai.transform_raw(
    broadband_denoised_raw, n_jobs=n_jobs, verbose=False
)

Since GEDAI algorithm automatically set the reference to average, you can reset the reference to the original channel after denoising to preserve the original reference scheme: adaptive_multiband_denoised_raw.set_eeg_reference(ref_channels="Cz", copy=False)

Visualize the results

plot_mne_style_overlay_interactive(raw, adaptive_multiband_denoised_raw, duration=15.0)

(<Figure size 1200x1750 with 1 Axes>, <Axes: xlabel='Time (s)', ylabel='Channels'>)

Estimated memory usage: 361 MB