Understanding the multiband extension of GEDAI

This tutorial demonstrates how to use multiband GEDAI. Multiband GEDAI is a frequency-specific denoising method that extends the generalized eigenvalue decomposition approach of GEDAI. Its approach focuses on isolating and removing artifacts within specific frequency bands. For that, the multiband GEDAI first decomposes the EEG data into its frequency components using wavelet transform, then applies GEDAI to each frequency band separately. Finally, the denoised frequency components are recombined to reconstruct the cleaned EEG signal.

Note

The purpose of this tutorial is to explain the different parameters of the MultibandGedai model and help you better understand the underlying algorithm. If you want to learn how to use Multiband GEDAI in a practical, end-to-end offline denoising workflow, please refer to the Practical Pipelines section.

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import matplotlib.pyplot as plt
from mne.io import read_raw

from gedai import MultibandGedai
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)

For simplicity, we will only use the first 30 seconds of the data in this tutorial. In practice, it is recommended to use the full recording for fitting the GEDAI model, as this allows the model to better capture the noise characteristics of the data.

raw.crop(0, 30)

	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	00:00:31 (HH:MM:SS)
	Sampling frequency	200.00 Hz
	Time points	6,001
	Channels
	EEG	27
	Head & sensor digitization	30 points
	Filters
	Highpass	0.00 Hz
	Lowpass	100.00 Hz

GEDAI

To use spectral GEDAI, we initialize the MultibandGedai object. By default, wavelet_level="auto" automatically determines the number of wavelet levels based on sampling frequency and the high-pass cutoff.

Broadband Pass (Two-Pass Filtering):

The broadband_pass=True parameter enables a two-stage hierarchical cleaning workflow:

1. Pass 1 (Broadband Pass): A full-spectrum spatial GEDAI filter is applied first to clean large, widespread artifacts (e.g. eye blinks, muscular bursts, head motion) across all channels simultaneously. This prevents massive artifacts from leaking across adjacent wavelet frequency subbands.

2. Pass 2 (Multiband Wavelet Pass): The pre-cleaned signal is decomposed into MODWT wavelet frequency bands, where each subband receives dedicated, fine-grained eigenvalue thresholding tailored to its specific frequency dynamics.

Enabling broadband_pass=True is recommended for recordings with prominent blinks or muscle contamination.

multiband_gedai = MultibandGedai(
    wavelet_type="haar",
    wavelet_level="auto",
    broadband_pass=True,
)

Model Fitting

The fitting process of spectral GEDAI is performed for each wavelet level (i.e., frequency band) to estimate the optimal threshold to distinguish between signal and noise components using SENSAI optimization (sensai_method="optimize").

multiband_gedai.fit_raw(raw, duration=2.0, n_jobs=n_jobs, verbose=True)

    343 x 343 full covariance (kind = 1) found.
[multiband.fit_raw] WARNING: Requested duration 2.000s adjusted to 5.120s (1024 samples) to satisfy wavelet level 9 requirements.
[multiband.fit_raw] INFO: Applying wavelet HP pre-filter (sub-0.50 Hz) and running broadband GEDAI pass...

Note

Since multiband GEDAI uses spectral decomposition, the fitting process will automatically adjust the epoch duration to ensure that each epoch contains a number of samples appropriate for the wavelet decomposition.

fig = multiband_gedai.plot_fit()
plt.show()

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Transform the Data (Denoising)

Once fitted, the Multiband GEDAI model cleans each frequency band using dual-stream cosine overlap-add blending (with 50% shifted streams) to eliminate any epoch boundary jump artifacts before recombining them.

denoised_raw = multiband_gedai.transform_raw(raw, n_jobs=n_jobs, verbose=False)

Warning

Since the Multiband GEDAI operates on epoched data internally during fitting, some frequency content more particularly in lower frequency bands may be not be captured properly if the epoch duration is too short. On the other hand, using very long epochs may prevent capturing short transient artifacts. Setting the wavelet_low_cutoff parameter to "auto" or to the acquisition high-pass cutoff excludes low frequency bands below the signal bandwidth.

plot_mne_style_overlay_interactive(raw, denoised_raw, duration=15.0)

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

Estimated memory usage: 330 MB