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/latest/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/latest/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
	Filename(s)	sub-010004_EC.fdt
	MNE object type	RawEEGLAB
	Measurement date	Unknown
	Participant	Unknown
	Experimenter	Unknown
	Acquisition
	Duration	00:00:31 (HH:MM:SS)
	Sampling frequency	250.00 Hz
	Time points	7,501
	Channels
	EEG	61
	Head & sensor digitization	64 points
	Filters
	Highpass	0.00 Hz
	Lowpass	125.00 Hz

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: 215 MB