Filtering and resampling data¶

Some artifacts are restricted to certain frequencies and can therefore be fixed by filtering. An artifact that typically affects only some frequencies is due to the power line.

Power-line noise is a noise created by the electrical network. It is composed of sharp peaks at 50Hz (or 60Hz depending on your geographical location). Some peaks may also be present at the harmonic frequencies, i.e. the integer multiples of the power-line frequency, e.g. 100Hz, 150Hz, … (or 120Hz, 180Hz, …).

This tutorial covers some basics of how to filter data in MNE-Python. For more in-depth information about filter design in general and in MNE-Python in particular, check out Background information on filtering.

import numpy as np
import mne
from mne.datasets import sample

data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
proj_fname = data_path + '/MEG/sample/sample_audvis_eog_proj.fif'

tmin, tmax = 0, 20  # use the first 20s of data

# Setup for reading the raw data (save memory by cropping the raw data

fmin, fmax = 2, 300  # look at frequencies between 2 and 300Hz
n_fft = 2048  # the FFT size (n_fft). Ideally a power of 2

# Pick a subset of channels (here for speed reason)
picks = mne.pick_types(raw.info, meg='mag', eeg=False, eog=False,

# Let's first check out all channel types
raw.plot_psd(area_mode='range', tmax=10.0, picks=picks, average=False)


Removing power-line noise with notch filtering¶

Removing power-line noise can be done with a Notch filter, directly on the Raw object, specifying an array of frequency to be cut off:

raw.notch_filter(np.arange(60, 241, 60), picks=picks, filter_length='auto',
phase='zero')
raw.plot_psd(area_mode='range', tmax=10.0, picks=picks, average=False)


Out:

fir_design defaults to "firwin2" in 0.15 but will change to "firwin" in 0.16, set it explicitly to avoid this warning.


Removing power-line noise with low-pass filtering¶

If you’re only interested in low frequencies, below the peaks of power-line noise you can simply low pass filter the data.

# low pass filtering below 50 Hz
raw.filter(None, 50., fir_design='firwin')
raw.plot_psd(area_mode='range', tmax=10.0, picks=picks, average=False)


High-pass filtering to remove slow drifts¶

To remove slow drifts, you can high pass.

Warning

In several applications such as event-related potential (ERP) and event-related field (ERF) analysis, high-pass filters with cutoff frequencies greater than 0.1 Hz are usually considered problematic since they significantly change the shape of the resulting averaged waveform (see examples in High-pass problems). In such applications, apply high-pass filters with caution.

raw.filter(1., None, fir_design='firwin')
raw.plot_psd(area_mode='range', tmax=10.0, picks=picks, average=False)


To do the low-pass and high-pass filtering in one step you can do a so-called band-pass filter by running the following:

# band-pass filtering in the range 1 Hz - 50 Hz
raw.filter(1, 50., fir_design='firwin')


Downsampling and decimation¶

When performing experiments where timing is critical, a signal with a high sampling rate is desired. However, having a signal with a much higher sampling rate than necessary needlessly consumes memory and slows down computations operating on the data. To avoid that, you can downsample your time series. Since downsampling raw data reduces the timing precision of events, it is recommended only for use in procedures that do not require optimal precision, e.g. computing EOG or ECG projectors on long recordings.

Note

A downsampling operation performs a low-pass (to prevent aliasing) followed by decimation, which selects every $$N^{th}$$ sample from the signal. See scipy.signal.resample() and scipy.signal.resample_poly() for examples.

Data resampling can be done with resample methods.

raw.resample(100, npad="auto")  # set sampling frequency to 100Hz
raw.plot_psd(area_mode='range', tmax=10.0, picks=picks)


To avoid this reduction in precision, the suggested pipeline for processing final data to be analyzed is:

1. low-pass the data with mne.io.Raw.filter().
2. Extract epochs with mne.Epochs.
3. Decimate the Epochs object using mne.Epochs.decimate() or the decim argument to the mne.Epochs object.

We also provide the convenience methods mne.Epochs.resample() and mne.Evoked.resample() to downsample or upsample data, but these are less optimal because they will introduce edge artifacts into every epoch, whereas filtering the raw data will only introduce edge artifacts only at the start and end of the recording.

Total running time of the script: ( 0 minutes 3.073 seconds)

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