GEDAI online🔗

This tutorial demonstrates how to use GEDAI for online (real-time) denoising.

import matplotlib.pyplot as plt
from mne.datasets import eegbci
from mne.io import concatenate_raws, read_raw_edf
from mne import make_fixed_length_epochs

from gedai import Gedai
from gedai.viz import plot_mne_style_overlay_interactive

subjects = [1]  # may vary
runs = [4, 8, 12]  # may vary
raw_fnames = eegbci.load_data(subjects, runs, update_path=True)
raws = [read_raw_edf(f, preload=True) for f in raw_fnames]
# Concatenate runs from the same subject
raw = concatenate_raws(raws)
# Make channel names follow standard conventions
eegbci.standardize(raw)

# Crop to the first 15 seconds for demonstration purposes
# (Remove or adjust this for full data analysis)
raw.crop(0, 30)
raw.pick("eeg").load_data().apply_proj()

# Apply average reference (standard preprocessing for EEG)
raw.set_eeg_reference("average", projection=False)

Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S001/S001R04.edf...
Setting channel info structure...
Creating raw.info structure...
Reading 0 ... 19999  =      0.000 ...   124.994 secs...
Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S001/S001R08.edf...
Setting channel info structure...
Creating raw.info structure...
Reading 0 ... 19999  =      0.000 ...   124.994 secs...
Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S001/S001R12.edf...
Setting channel info structure...
Creating raw.info structure...
Reading 0 ... 19999  =      0.000 ...   124.994 secs...
No projector specified for this dataset. Please consider the method self.add_proj.
EEG channel type selected for re-referencing
Applying average reference.
Applying a custom ('EEG',) reference.

	General
	Filename(s)	S001R04.edf
	MNE object type	RawEDF
	Measurement date	2009-08-12 at 16:15:00 UTC
	Participant	X
	Experimenter	Unknown
	Acquisition
	Duration	00:00:31 (HH:MM:SS)
	Sampling frequency	160.00 Hz
	Time points	4,801
	Channels
	EEG
	Head & sensor digitization	Not available
	Filters
	Highpass	0.00 Hz
	Lowpass	80.00 Hz

For this example, we will use the standard broadband GEDAI. However, the same principles apply to the spectral GEDAI as well. For real-time applications, data is typically processed in chunks (e.g., sliding windows). To simulate this, we will first convert the raw data into overlapping epochs.

epochs = make_fixed_length_epochs(raw, duration=1.0, overlap=0.5, preload=True)

Not setting metadata
59 matching events found
No baseline correction applied
0 projection items activated
Using data from preloaded Raw for 59 events and 160 original time points ...
0 bad epochs dropped

We first fit need to fit the GEDAI model on some initial data segment. This segment should be representative of the data to be denoised, including typical artifacts. A common approach is to use a baseline period at the beginning of the recording for this purpose.

gedai = Gedai()

# In this example, we use the first 10 seconds (i.e., first 20 epochs) for model fitting.
# The baseline period
baseline_epochs = epochs[:20]
gedai.fit_epochs(baseline_epochs, verbose=True)

After fitting, we can apply the fitted GEDAI model to each incoming epoch for denoising.

for i in range(20, len(epochs)):
    epoch = epochs[i : i + 1]  # Get the current epoch
    denoised_epoch = gedai.transform_epochs(epoch)

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1 matching events found
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Total running time of the script: (0 minutes 35.601 seconds)

Estimated memory usage: 227 MB

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