.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_mne_dataset_example.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here <sphx_glr_download_auto_examples_plot_mne_dataset_example.py>`
to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_plot_mne_dataset_example.py:
MNE Dataset Example
===================
.. GENERATED FROM PYTHON SOURCE LINES 6-8
This example shows how to convert data from mne.Raws or mne.Epochs to a
braindecode compatible data format.
.. GENERATED FROM PYTHON SOURCE LINES 8-18
.. code-block:: default
# Authors: Lukas Gemein <l.gemein@gmail.com>
#
# License: BSD (3-clause)
import mne
from braindecode.datasets import (
create_from_mne_raw, create_from_mne_epochs)
.. GENERATED FROM PYTHON SOURCE LINES 19-20
First, fetch some data using mne:
.. GENERATED FROM PYTHON SOURCE LINES 20-35
.. code-block:: default
# 5, 6, 7, 10, 13, 14 are codes for executed and imagined hands/feet
subject_id = 22
event_codes = [5, 6, 9, 10, 13, 14]
# event_codes = [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
# This will download the files if you don't have them yet,
# and then return the paths to the files.
physionet_paths = mne.datasets.eegbci.load_data(
subject_id, event_codes, update_path=False)
# Load each of the files
parts = [mne.io.read_raw_edf(path, preload=True, stim_channel='auto')
for path in physionet_paths]
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R05.edf...
EDF file detected
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/S022/S022R06.edf...
EDF file detected
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/S022/S022R09.edf...
EDF file detected
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/S022/S022R10.edf...
EDF file detected
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/S022/S022R13.edf...
EDF file detected
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/S022/S022R14.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Reading 0 ... 19999 = 0.000 ... 124.994 secs...
.. GENERATED FROM PYTHON SOURCE LINES 36-37
Convert Raw objects to a compatible data format:
.. GENERATED FROM PYTHON SOURCE LINES 37-49
.. code-block:: default
descriptions = [{"event_code": code, "subject": subject_id}
for code in event_codes]
windows_dataset = create_from_mne_raw(
parts,
trial_start_offset_samples=0,
trial_stop_offset_samples=0,
window_size_samples=500,
window_stride_samples=500,
drop_last_window=False,
descriptions=descriptions,
)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
Used Annotations descriptions: ['T0', 'T1', 'T2']
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
60 matching events found
No baseline correction applied
0 projection items activated
Loading data for 60 events and 500 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['T0', 'T1', 'T2']
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
60 matching events found
No baseline correction applied
0 projection items activated
Loading data for 60 events and 500 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['T0', 'T1', 'T2']
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
60 matching events found
No baseline correction applied
0 projection items activated
Loading data for 60 events and 500 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['T0', 'T1', 'T2']
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
60 matching events found
No baseline correction applied
0 projection items activated
Loading data for 60 events and 500 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['T0', 'T1', 'T2']
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
60 matching events found
No baseline correction applied
0 projection items activated
Loading data for 60 events and 500 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['T0', 'T1', 'T2']
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
60 matching events found
No baseline correction applied
0 projection items activated
Loading data for 60 events and 500 original time points ...
0 bad epochs dropped
.. GENERATED FROM PYTHON SOURCE LINES 50-51
If trials were already cut beforehand and are available as mne.Epochs:
.. GENERATED FROM PYTHON SOURCE LINES 51-61
.. code-block:: default
list_of_epochs = [mne.Epochs(raw, [[0, 0, 0]], tmin=0, baseline=None)
for raw in parts]
windows_dataset = create_from_mne_epochs(
list_of_epochs,
window_size_samples=50,
window_stride_samples=50,
drop_last_window=False
)
windows_dataset.description
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
Not setting metadata
Not setting metadata
1 matching events found
No baseline correction applied
0 projection items activated
Not setting metadata
Not setting metadata
1 matching events found
No baseline correction applied
0 projection items activated
Not setting metadata
Not setting metadata
1 matching events found
No baseline correction applied
0 projection items activated
Not setting metadata
Not setting metadata
1 matching events found
No baseline correction applied
0 projection items activated
Not setting metadata
Not setting metadata
1 matching events found
No baseline correction applied
0 projection items activated
Not setting metadata
Not setting metadata
1 matching events found
No baseline correction applied
0 projection items activated
Creating RawArray with float64 data, n_channels=64, n_times=81
Range : 0 ... 80 = 0.000 ... 0.500 secs
Ready.
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
2 matching events found
No baseline correction applied
0 projection items activated
Loading data for 2 events and 50 original time points ...
0 bad epochs dropped
Creating RawArray with float64 data, n_channels=64, n_times=81
Range : 0 ... 80 = 0.000 ... 0.500 secs
Ready.
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
2 matching events found
No baseline correction applied
0 projection items activated
Loading data for 2 events and 50 original time points ...
0 bad epochs dropped
Creating RawArray with float64 data, n_channels=64, n_times=81
Range : 0 ... 80 = 0.000 ... 0.500 secs
Ready.
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
2 matching events found
No baseline correction applied
0 projection items activated
Loading data for 2 events and 50 original time points ...
0 bad epochs dropped
Creating RawArray with float64 data, n_channels=64, n_times=81
Range : 0 ... 80 = 0.000 ... 0.500 secs
Ready.
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
2 matching events found
No baseline correction applied
0 projection items activated
Loading data for 2 events and 50 original time points ...
0 bad epochs dropped
Creating RawArray with float64 data, n_channels=64, n_times=81
Range : 0 ... 80 = 0.000 ... 0.500 secs
Ready.
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
2 matching events found
No baseline correction applied
0 projection items activated
Loading data for 2 events and 50 original time points ...
0 bad epochs dropped
Creating RawArray with float64 data, n_channels=64, n_times=81
Range : 0 ... 80 = 0.000 ... 0.500 secs
Ready.
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
2 matching events found
No baseline correction applied
0 projection items activated
Loading data for 2 events and 50 original time points ...
0 bad epochs dropped
.. raw:: html
<div class="output_subarea output_html rendered_html output_result">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>0</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>None</td>
</tr>
<tr>
<th>1</th>
<td>None</td>
</tr>
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<th>2</th>
<td>None</td>
</tr>
<tr>
<th>3</th>
<td>None</td>
</tr>
<tr>
<th>4</th>
<td>None</td>
</tr>
<tr>
<th>5</th>
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</tbody>
</table>
</div>
</div>
<br />
<br />
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 0.827 seconds)
**Estimated memory usage:** 77 MB
.. _sphx_glr_download_auto_examples_plot_mne_dataset_example.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_mne_dataset_example.py <plot_mne_dataset_example.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_mne_dataset_example.ipynb <plot_mne_dataset_example.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_