Note

Click here to download the full example code

# Frequency and time-frequency sensor analysis¶

The objective is to show you how to explore the spectral content of your data (frequency and time-frequency). Here we’ll work on Epochs.

We will use this dataset: Somatosensory. It contains so-called event related synchronizations (ERS) / desynchronizations (ERD) in the beta band.

```
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
# Richard Höchenberger <richard.hoechenberger@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.time_frequency import tfr_morlet, psd_multitaper, psd_welch
from mne.datasets import somato
```

Set parameters

```
data_path = somato.data_path()
subject = '01'
task = 'somato'
raw_fname = op.join(data_path, 'sub-{}'.format(subject), 'meg',
'sub-{}_task-{}_meg.fif'.format(subject, task))
# Setup for reading the raw data
raw = mne.io.read_raw_fif(raw_fname)
events = mne.find_events(raw, stim_channel='STI 014')
# picks MEG gradiometers
picks = mne.pick_types(raw.info, meg='grad', eeg=False, eog=True, stim=False)
# Construct Epochs
event_id, tmin, tmax = 1, -1., 3.
baseline = (None, 0)
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=baseline, reject=dict(grad=4000e-13, eog=350e-6),
preload=True)
epochs.resample(200., npad='auto') # resample to reduce computation time
```

Out:

```
Opening raw data file /home/circleci/mne_data/MNE-somato-data/sub-01/meg/sub-01_task-somato_meg.fif...
Range : 237600 ... 506999 = 791.189 ... 1688.266 secs
Ready.
111 events found
Event IDs: [1]
Not setting metadata
Not setting metadata
111 matching events found
Setting baseline interval to [-0.9989760657919393, 0.0] sec
Applying baseline correction (mode: mean)
0 projection items activated
Loading data for 111 events and 1202 original time points ...
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
3 bad epochs dropped
```

## Frequency analysis¶

We start by exploring the frequence content of our epochs.

Let’s first check out all channel types by averaging across epochs.

```
epochs.plot_psd(fmin=2., fmax=40., average=True, spatial_colors=False)
```

Out:

```
Using multitaper spectrum estimation with 7 DPSS windows
```

Now let’s take a look at the spatial distributions of the PSD.

```
epochs.plot_psd_topomap(ch_type='grad', normalize=True)
```

Out:

```
Using multitaper spectrum estimation with 7 DPSS windows
```

Alternatively, you can also create PSDs from Epochs objects with functions
that start with `psd_`

such as
`mne.time_frequency.psd_multitaper()`

and
`mne.time_frequency.psd_welch()`

.

```
f, ax = plt.subplots()
psds, freqs = psd_multitaper(epochs, fmin=2, fmax=40, n_jobs=1)
psds = 10. * np.log10(psds)
psds_mean = psds.mean(0).mean(0)
psds_std = psds.mean(0).std(0)
ax.plot(freqs, psds_mean, color='k')
ax.fill_between(freqs, psds_mean - psds_std, psds_mean + psds_std,
color='k', alpha=.5)
ax.set(title='Multitaper PSD (gradiometers)', xlabel='Frequency (Hz)',
ylabel='Power Spectral Density (dB)')
plt.show()
```

Out:

```
Using multitaper spectrum estimation with 7 DPSS windows
```

Notably, `mne.time_frequency.psd_welch()`

supports the keyword argument
`average`

, which specifies how to estimate the PSD based on the individual
windowed segments. The default is `average='mean'`

, which simply calculates
the arithmetic mean across segments. Specifying `average='median'`

, in
contrast, returns the PSD based on the median of the segments (corrected for
bias relative to the mean), which is a more robust measure.

```
# Estimate PSDs based on "mean" and "median" averaging for comparison.
kwargs = dict(fmin=2, fmax=40, n_jobs=1)
psds_welch_mean, freqs_mean = psd_welch(epochs, average='mean', **kwargs)
psds_welch_median, freqs_median = psd_welch(epochs, average='median', **kwargs)
# Convert power to dB scale.
psds_welch_mean = 10 * np.log10(psds_welch_mean)
psds_welch_median = 10 * np.log10(psds_welch_median)
# We will only plot the PSD for a single sensor in the first epoch.
ch_name = 'MEG 0122'
ch_idx = epochs.info['ch_names'].index(ch_name)
epo_idx = 0
_, ax = plt.subplots()
ax.plot(freqs_mean, psds_welch_mean[epo_idx, ch_idx, :], color='k',
ls='-', label='mean of segments')
ax.plot(freqs_median, psds_welch_median[epo_idx, ch_idx, :], color='k',
ls='--', label='median of segments')
ax.set(title='Welch PSD ({}, Epoch {})'.format(ch_name, epo_idx),
xlabel='Frequency (Hz)', ylabel='Power Spectral Density (dB)')
ax.legend(loc='upper right')
plt.show()
```

Out:

```
Effective window size : 1.280 (s)
Effective window size : 1.280 (s)
```

Lastly, we can also retrieve the unaggregated segments by passing
`average=None`

to `mne.time_frequency.psd_welch()`

. The dimensions of
the returned array are `(n_epochs, n_sensors, n_freqs, n_segments)`

.

```
psds_welch_unagg, freqs_unagg = psd_welch(epochs, average=None, **kwargs)
print(psds_welch_unagg.shape)
```

Out:

```
Effective window size : 1.280 (s)
(108, 204, 49, 3)
```

## Time-frequency analysis: power and inter-trial coherence¶

We now compute time-frequency representations (TFRs) from our Epochs. We’ll look at power and inter-trial coherence (ITC).

To this we’ll use the function `mne.time_frequency.tfr_morlet()`

but you can also use `mne.time_frequency.tfr_multitaper()`

or `mne.time_frequency.tfr_stockwell()`

.

Note

The `decim`

parameter reduces the sampling rate of the time-frequency
decomposition by the defined factor. This is usually done to reduce
memory usage. For more information refer to the documentation of
`mne.time_frequency.tfr_morlet()`

.

define frequencies of interest (log-spaced)

## Inspect power¶

Note

The generated figures are interactive. In the topo you can click on an image to visualize the data for one sensor. You can also select a portion in the time-frequency plane to obtain a topomap for a certain time-frequency region.

```
power.plot_topo(baseline=(-0.5, 0), mode='logratio', title='Average power')
power.plot([82], baseline=(-0.5, 0), mode='logratio', title=power.ch_names[82])
fig, axis = plt.subplots(1, 2, figsize=(7, 4))
power.plot_topomap(ch_type='grad', tmin=0.5, tmax=1.5, fmin=8, fmax=12,
baseline=(-0.5, 0), mode='logratio', axes=axis[0],
title='Alpha', show=False)
power.plot_topomap(ch_type='grad', tmin=0.5, tmax=1.5, fmin=13, fmax=25,
baseline=(-0.5, 0), mode='logratio', axes=axis[1],
title='Beta', show=False)
mne.viz.tight_layout()
plt.show()
```

Out:

```
Applying baseline correction (mode: logratio)
Applying baseline correction (mode: logratio)
Applying baseline correction (mode: logratio)
Applying baseline correction (mode: logratio)
```

## Joint Plot¶

You can also create a joint plot showing both the aggregated TFR across channels and topomaps at specific times and frequencies to obtain a quick overview regarding oscillatory effects across time and space.

```
power.plot_joint(baseline=(-0.5, 0), mode='mean', tmin=-.5, tmax=2,
timefreqs=[(.5, 10), (1.3, 8)])
```

Out:

```
Applying baseline correction (mode: mean)
Applying baseline correction (mode: mean)
Applying baseline correction (mode: mean)
```

## Inspect ITC¶

```
itc.plot_topo(title='Inter-Trial coherence', vmin=0., vmax=1., cmap='Reds')
```

Out:

```
No baseline correction applied
```

Note

Baseline correction can be applied to power or done in plots. To illustrate the baseline correction in plots, the next line is commented power.apply_baseline(baseline=(-0.5, 0), mode=’logratio’)

## Exercise¶

Visualize the inter-trial coherence values as topomaps as done with power.

**Total running time of the script:** ( 1 minutes 4.931 seconds)

**Estimated memory usage:** 555 MB