Resampling

This example demonstrates how to resample a recording.

Note

The lemon datasets used in this tutorial is composed of EEGLAB files. To use the MNE reader mne.io.read_raw_eeglab(), the pymatreader optional dependency is required. Use the following installation method appropriate for your environment:

• pip install pymatreader

• conda install -c conda-forge pymatreader

Note that an environment created via the MNE installers includes pymatreader by default.

import mne
from mne.io import read_raw_eeglab

from pycrostates.datasets import lemon


raw_fname = lemon.data_path(subject_id='010004', condition='EC')
raw = read_raw_eeglab(raw_fname, preload=True)
raw.crop(0, 30)

raw.pick('eeg')
raw.set_eeg_reference('average')

/home/docs/checkouts/readthedocs.org/user_builds/pycrostates/checkouts/0.6.1/tutorials/preprocessing/10_resampling.py:32: RuntimeWarning: Limited 1 annotation(s) that were expanding outside the data range.
  raw = read_raw_eeglab(raw_fname, preload=True)
/home/docs/checkouts/readthedocs.org/user_builds/pycrostates/checkouts/0.6.1/tutorials/preprocessing/10_resampling.py:32: RuntimeWarning: The data contains 'boundary' events, indicating data discontinuities. Be cautious of filtering and epoching around these events.
  raw = read_raw_eeglab(raw_fname, preload=True)

General

Measurement date	Unknown
Experimenter	Unknown
Participant	Unknown

Channels

Digitized points	64 points
Good channels	61 EEG
Bad channels	None
EOG channels	Not available
ECG channels	Not available

Data

Sampling frequency	250.00 Hz
Highpass	0.00 Hz
Lowpass	125.00 Hz
Filenames	sub-010004_EC.fdt
Duration	00:00:30 (HH:MM:SS)

The resample() function draws n_resamples of n_samples from a recording where n_samples defines the number of samples contained in each epoch and n_resamples defines the number of epochs to draw.

from pycrostates.preprocessing import resample
resamples = resample(raw, n_resamples=3, n_samples=150, random_state=40)
resamples

[< ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >]

The coverage parameter can be used to automatically compute one of the two previous parameters based on the amount of original data to cover. For example, by setting n_resamples and coverage:

resamples = resample(raw, n_resamples=3, coverage=0.5, random_state=40)
resamples

[< ChData | 1250 samples >, < ChData | 1250 samples >, < ChData | 1250 samples >]

or by setting n_samples and coverage:

resamples = resample(raw, n_samples=1000, coverage=0.5, random_state=40)
resamples

[< ChData | 1000 samples >, < ChData | 1000 samples >, < ChData | 1000 samples >]

Finally, this function can be used to resample Epochs.

epochs = mne.make_fixed_length_epochs(raw, duration=2, preload=True)
resamples = resample(epochs, n_samples=150, coverage=0.5, random_state=40)
resamples

[< ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >, < ChData | 150 samples >]

Estimated memory usage: 239 MB