MNE python for EEG analysis
Contents
MNE python for EEG analysis¶
What is MNE?
A way for universities to save a lot cash that would otherwise be invested into matlab licenses, probably,
But more generally speaking MNE is an open-source Python package for working with MEG, EEG, NIRS, with extensive documentation, tutorials and API references. The package has an active and engaged developer community on Github, as well as in the discourse forum, where you can turn to with problems, more complex questions or analysis strategies that the provided tutorials may not adress (after reading the FAQ, of course). If any of the terms used in this tutorial are unclear you can further check out the glossary.
MNE should provide anything you’d need for for exploring, visualizing, and analyzing neurophysiological data.
Basics¶
Let’s jump right in. We’ll be dealing with EEG data, but the same syntax should apply for most other data supported by MNE.
We’re first gonna import the necessary libraries for this section.
# import necessary libraries/functions
import os # for directory navigation
import mne
from mne_bids import BIDSPath, write_raw_bids, print_dir_tree, make_report, read_raw_bids
import pandas as pd # mostly for saving data as csv
# this allows for interactive pop-up plots
#%matplotlib qt
# allows for rendering in output-cell
%matplotlib notebook
Loading data¶
MNE uses the mne.io.read_raw_*() function to import raw data. The MNE standard is the .fif dataformat, but how data is stored/generated is generally dependent on the software used to record the EEG. MNE provides a list of supported dataformats and the corresponding version of the .read_raw() function you’ll to use here. For information on how to import data from different recoding systems see here.
Head to the MNE tutorials: The Raw data structure: continuous data for some more in-depth explanations from the developers.
First we’ll download the sample dataset using the mne.datasets.sample.data_path() function. .sample.data_path(~/path_to_data) will download the sample data automatically when the data is not found in the provided path. In the downloaded dataset directory we’ll next look for the “sample_audvis_raw.fif” file containing the EEG data in the MEG/sample/ path.
# load some sample data for convenience, overwrite previous data
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
As you might have guessed, to import the “sample_audvis_raw.fif” file we’ll use the mne.io.read_raw_fif() function. Setting the preload parameter to “True” will load the raw data into memory, which is needed for data manipulation but may take up large amounts of memory depending on the size of your raw data.
raw = mne.io.read_raw_fif(sample_data_raw_file, preload=True)
Opening raw data file /home/michael/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
Read a total of 3 projection items:
PCA-v1 (1 x 102) idle
PCA-v2 (1 x 102) idle
PCA-v3 (1 x 102) idle
Range : 25800 ... 192599 = 42.956 ... 320.670 secs
Ready.
Reading 0 ... 166799 = 0.000 ... 277.714 secs...
Exploring the raw object¶
Now we can explore the raw data object. Using the raw.info attribute, we get info on the contained data, such as the number of channels, the sampling frequency with which the data was collected etc., as well as some metadata (such as the measurement date). For an explanation of the listed Attributes see the notes in the API reference.
raw.info
| Measurement date | December 03, 2002 19:01:10 GMT |
|---|---|
| Experimenter | MEG | Participant | Unknown |
| Digitized points | 0 points |
| Good channels | 204 Gradiometers, 102 Magnetometers, 9 Stimulus, 60 EEG, 1 EOG |
| Bad channels | MEG 2443, EEG 053 |
| EOG channels | EOG 061 |
| ECG channels | Not available |
| Sampling frequency | 600.61 Hz |
| Highpass | 0.10 Hz |
| Lowpass | 172.18 Hz |
| Projections | PCA-v1 : off PCA-v2 : off PCA-v3 : off |
The .info.keys() function displays a dictionary containing the possible parameters, we can pass to the raw.info object.
raw.info.keys()
dict_keys(['file_id', 'events', 'hpi_results', 'hpi_meas', 'subject_info', 'device_info', 'helium_info', 'hpi_subsystem', 'proc_history', 'meas_id', 'experimenter', 'description', 'proj_id', 'proj_name', 'meas_date', 'utc_offset', 'sfreq', 'highpass', 'lowpass', 'line_freq', 'gantry_angle', 'chs', 'dev_head_t', 'ctf_head_t', 'dev_ctf_t', 'dig', 'bads', 'ch_names', 'nchan', 'projs', 'comps', 'acq_pars', 'acq_stim', 'custom_ref_applied', 'xplotter_layout', 'kit_system_id'])
Using these parameters we can access different parts of the .info, such as the events contained in the data
raw.info['events']
[{'channels': array([307, 308, 309, 310, 311, 312, 313, 314, 315], dtype=int32),
'list': array([ 16422, 0, 64, ..., 168680, 32, 0], dtype=int32)}]
or a list of the names of all the included channels.
raw.info['ch_names']
['MEG 0113',
'MEG 0112',
'MEG 0111',
'MEG 0122',
'MEG 0123',
'MEG 0121',
'MEG 0132',
'MEG 0133',
'MEG 0131',
'MEG 0143',
'MEG 0142',
'MEG 0141',
'MEG 0213',
'MEG 0212',
'MEG 0211',
'MEG 0222',
'MEG 0223',
'MEG 0221',
'MEG 0232',
'MEG 0233',
'MEG 0231',
'MEG 0243',
'MEG 0242',
'MEG 0241',
'MEG 0313',
'MEG 0312',
'MEG 0311',
'MEG 0322',
'MEG 0323',
'MEG 0321',
'MEG 0333',
'MEG 0332',
'MEG 0331',
'MEG 0343',
'MEG 0342',
'MEG 0341',
'MEG 0413',
'MEG 0412',
'MEG 0411',
'MEG 0422',
'MEG 0423',
'MEG 0421',
'MEG 0432',
'MEG 0433',
'MEG 0431',
'MEG 0443',
'MEG 0442',
'MEG 0441',
'MEG 0513',
'MEG 0512',
'MEG 0511',
'MEG 0523',
'MEG 0522',
'MEG 0521',
'MEG 0532',
'MEG 0533',
'MEG 0531',
'MEG 0542',
'MEG 0543',
'MEG 0541',
'MEG 0613',
'MEG 0612',
'MEG 0611',
'MEG 0622',
'MEG 0623',
'MEG 0621',
'MEG 0633',
'MEG 0632',
'MEG 0631',
'MEG 0642',
'MEG 0643',
'MEG 0641',
'MEG 0713',
'MEG 0712',
'MEG 0711',
'MEG 0723',
'MEG 0722',
'MEG 0721',
'MEG 0733',
'MEG 0732',
'MEG 0731',
'MEG 0743',
'MEG 0742',
'MEG 0741',
'MEG 0813',
'MEG 0812',
'MEG 0811',
'MEG 0822',
'MEG 0823',
'MEG 0821',
'MEG 0913',
'MEG 0912',
'MEG 0911',
'MEG 0923',
'MEG 0922',
'MEG 0921',
'MEG 0932',
'MEG 0933',
'MEG 0931',
'MEG 0942',
'MEG 0943',
'MEG 0941',
'MEG 1013',
'MEG 1012',
'MEG 1011',
'MEG 1023',
'MEG 1022',
'MEG 1021',
'MEG 1032',
'MEG 1033',
'MEG 1031',
'MEG 1043',
'MEG 1042',
'MEG 1041',
'MEG 1112',
'MEG 1113',
'MEG 1111',
'MEG 1123',
'MEG 1122',
'MEG 1121',
'MEG 1133',
'MEG 1132',
'MEG 1131',
'MEG 1142',
'MEG 1143',
'MEG 1141',
'MEG 1213',
'MEG 1212',
'MEG 1211',
'MEG 1223',
'MEG 1222',
'MEG 1221',
'MEG 1232',
'MEG 1233',
'MEG 1231',
'MEG 1243',
'MEG 1242',
'MEG 1241',
'MEG 1312',
'MEG 1313',
'MEG 1311',
'MEG 1323',
'MEG 1322',
'MEG 1321',
'MEG 1333',
'MEG 1332',
'MEG 1331',
'MEG 1342',
'MEG 1343',
'MEG 1341',
'MEG 1412',
'MEG 1413',
'MEG 1411',
'MEG 1423',
'MEG 1422',
'MEG 1421',
'MEG 1433',
'MEG 1432',
'MEG 1431',
'MEG 1442',
'MEG 1443',
'MEG 1441',
'MEG 1512',
'MEG 1513',
'MEG 1511',
'MEG 1522',
'MEG 1523',
'MEG 1521',
'MEG 1533',
'MEG 1532',
'MEG 1531',
'MEG 1543',
'MEG 1542',
'MEG 1541',
'MEG 1613',
'MEG 1612',
'MEG 1611',
'MEG 1622',
'MEG 1623',
'MEG 1621',
'MEG 1632',
'MEG 1633',
'MEG 1631',
'MEG 1643',
'MEG 1642',
'MEG 1641',
'MEG 1713',
'MEG 1712',
'MEG 1711',
'MEG 1722',
'MEG 1723',
'MEG 1721',
'MEG 1732',
'MEG 1733',
'MEG 1731',
'MEG 1743',
'MEG 1742',
'MEG 1741',
'MEG 1813',
'MEG 1812',
'MEG 1811',
'MEG 1822',
'MEG 1823',
'MEG 1821',
'MEG 1832',
'MEG 1833',
'MEG 1831',
'MEG 1843',
'MEG 1842',
'MEG 1841',
'MEG 1912',
'MEG 1913',
'MEG 1911',
'MEG 1923',
'MEG 1922',
'MEG 1921',
'MEG 1932',
'MEG 1933',
'MEG 1931',
'MEG 1943',
'MEG 1942',
'MEG 1941',
'MEG 2013',
'MEG 2012',
'MEG 2011',
'MEG 2023',
'MEG 2022',
'MEG 2021',
'MEG 2032',
'MEG 2033',
'MEG 2031',
'MEG 2042',
'MEG 2043',
'MEG 2041',
'MEG 2113',
'MEG 2112',
'MEG 2111',
'MEG 2122',
'MEG 2123',
'MEG 2121',
'MEG 2133',
'MEG 2132',
'MEG 2131',
'MEG 2143',
'MEG 2142',
'MEG 2141',
'MEG 2212',
'MEG 2213',
'MEG 2211',
'MEG 2223',
'MEG 2222',
'MEG 2221',
'MEG 2233',
'MEG 2232',
'MEG 2231',
'MEG 2242',
'MEG 2243',
'MEG 2241',
'MEG 2312',
'MEG 2313',
'MEG 2311',
'MEG 2323',
'MEG 2322',
'MEG 2321',
'MEG 2332',
'MEG 2333',
'MEG 2331',
'MEG 2343',
'MEG 2342',
'MEG 2341',
'MEG 2412',
'MEG 2413',
'MEG 2411',
'MEG 2423',
'MEG 2422',
'MEG 2421',
'MEG 2433',
'MEG 2432',
'MEG 2431',
'MEG 2442',
'MEG 2443',
'MEG 2441',
'MEG 2512',
'MEG 2513',
'MEG 2511',
'MEG 2522',
'MEG 2523',
'MEG 2521',
'MEG 2533',
'MEG 2532',
'MEG 2531',
'MEG 2543',
'MEG 2542',
'MEG 2541',
'MEG 2612',
'MEG 2613',
'MEG 2611',
'MEG 2623',
'MEG 2622',
'MEG 2621',
'MEG 2633',
'MEG 2632',
'MEG 2631',
'MEG 2642',
'MEG 2643',
'MEG 2641',
'STI 001',
'STI 002',
'STI 003',
'STI 004',
'STI 005',
'STI 006',
'STI 014',
'STI 015',
'STI 016',
'EEG 001',
'EEG 002',
'EEG 003',
'EEG 004',
'EEG 005',
'EEG 006',
'EEG 007',
'EEG 008',
'EEG 009',
'EEG 010',
'EEG 011',
'EEG 012',
'EEG 013',
'EEG 014',
'EEG 015',
'EEG 016',
'EEG 017',
'EEG 018',
'EEG 019',
'EEG 020',
'EEG 021',
'EEG 022',
'EEG 023',
'EEG 024',
'EEG 025',
'EEG 026',
'EEG 027',
'EEG 028',
'EEG 029',
'EEG 030',
'EEG 031',
'EEG 032',
'EEG 033',
'EEG 034',
'EEG 035',
'EEG 036',
'EEG 037',
'EEG 038',
'EEG 039',
'EEG 040',
'EEG 041',
'EEG 042',
'EEG 043',
'EEG 044',
'EEG 045',
'EEG 046',
'EEG 047',
'EEG 048',
'EEG 049',
'EEG 050',
'EEG 051',
'EEG 052',
'EEG 053',
'EEG 054',
'EEG 055',
'EEG 056',
'EEG 057',
'EEG 058',
'EEG 059',
'EEG 060',
'EOG 061']
To visualize the raw data, the .plot() function can be used to open an interactive plot. See the notes section of .plot() for more info how to navigate interactive plots created by MNE. (Note: To view interactive output plots run this chapter as a jupyter notebook or via binder)
# simple plotting function
raw.plot();
Using matplotlib as 2D backend.
Opening raw-browser...
To display the corresponindg positions of the channels contained in the raw object use the .plot_sensors() function.
raw.plot_sensors(show_names=True);
which looks a bit crowded. Consulting the “Good channels” attribute in the .info reveals that we have both MEG and EEG data in our raw object.
raw.info
| Measurement date | December 03, 2002 19:01:10 GMT |
|---|---|
| Experimenter | MEG | Participant | Unknown |
| Digitized points | 0 points |
| Good channels | 204 Gradiometers, 102 Magnetometers, 9 Stimulus, 60 EEG, 1 EOG |
| Bad channels | MEG 2443, EEG 053 |
| EOG channels | EOG 061 |
| ECG channels | Not available |
| Sampling frequency | 600.61 Hz |
| Highpass | 0.10 Hz |
| Lowpass | 172.18 Hz |
| Projections | PCA-v1 : off PCA-v2 : off PCA-v3 : off |
With the ch_type parameter we can select which channels we want to plot. Conventional nomenclature would dictate that channels are named after their respective position on the skull (see International 10–20 system (Wikipedia)), unfortunately for the sample data we’ve only a numbered list of EEG channels. You may further notice that channels markes as “bad” in the .info are displayed in red.
raw.plot_sensors(show_names=True, ch_type='eeg');
Using the "kind" parameter we can further display our channel positions in 3d.
raw.plot_sensors(show_names=True, ch_type='eeg', kind='3d');
There are multiple ways to access the data contained in the raw object. The tuple syntax shown below for example.
type(raw[:])
tuple
raw[:]
(array([[ 9.64355481e-12, 0.00000000e+00, 0.00000000e+00, ...,
-1.92871096e-12, 2.89306644e-12, 3.85742192e-12],
[-4.82177740e-12, -2.89306644e-12, -9.64355481e-13, ...,
-9.64355481e-13, -9.64355481e-13, -1.92871096e-12],
[ 1.01074222e-13, 6.31713890e-14, 7.58056668e-14, ...,
-4.80102556e-13, -6.06445334e-13, -5.93811056e-13],
...,
[ 3.88542173e-05, 4.07510373e-05, 4.09957883e-05, ...,
6.72453304e-05, 6.68782039e-05, 6.91421504e-05],
[ 6.58391126e-05, 6.80025648e-05, 6.81779798e-05, ...,
8.51932390e-05, 8.58948991e-05, 8.89938982e-05],
[ 2.85661012e-04, 2.83699953e-04, 2.80431520e-04, ...,
2.64089357e-04, 2.62781984e-04, 2.57552492e-04]]),
array([0.00000000e+00, 1.66496011e-03, 3.32992022e-03, ...,
2.77710351e+02, 2.77712016e+02, 2.77713681e+02]))
Accessing the raw data this way reveals that the data is organized into two numpy arrays, one containing the amplitude in each channel for each sample and the other the corresponding times, resulting in an (n_chans × n_samps) and an (1 × n_samps) matrix respectively.
raw[:][0] is the (n_chans × n_samps) matrix containing our EEG-amplitudes
print(type(raw[:][0]))
<class 'numpy.ndarray'>
raw[:][0]
array([[ 9.64355481e-12, 0.00000000e+00, 0.00000000e+00, ...,
-1.92871096e-12, 2.89306644e-12, 3.85742192e-12],
[-4.82177740e-12, -2.89306644e-12, -9.64355481e-13, ...,
-9.64355481e-13, -9.64355481e-13, -1.92871096e-12],
[ 1.01074222e-13, 6.31713890e-14, 7.58056668e-14, ...,
-4.80102556e-13, -6.06445334e-13, -5.93811056e-13],
...,
[ 3.88542173e-05, 4.07510373e-05, 4.09957883e-05, ...,
6.72453304e-05, 6.68782039e-05, 6.91421504e-05],
[ 6.58391126e-05, 6.80025648e-05, 6.81779798e-05, ...,
8.51932390e-05, 8.58948991e-05, 8.89938982e-05],
[ 2.85661012e-04, 2.83699953e-04, 2.80431520e-04, ...,
2.64089357e-04, 2.62781984e-04, 2.57552492e-04]])
print(raw[:][0]) # 1 array containing (n_chans × n_samps)
print(raw[:][0].shape)
[[ 9.64355481e-12 0.00000000e+00 0.00000000e+00 ... -1.92871096e-12
2.89306644e-12 3.85742192e-12]
[-4.82177740e-12 -2.89306644e-12 -9.64355481e-13 ... -9.64355481e-13
-9.64355481e-13 -1.92871096e-12]
[ 1.01074222e-13 6.31713890e-14 7.58056668e-14 ... -4.80102556e-13
-6.06445334e-13 -5.93811056e-13]
...
[ 3.88542173e-05 4.07510373e-05 4.09957883e-05 ... 6.72453304e-05
6.68782039e-05 6.91421504e-05]
[ 6.58391126e-05 6.80025648e-05 6.81779798e-05 ... 8.51932390e-05
8.58948991e-05 8.89938982e-05]
[ 2.85661012e-04 2.83699953e-04 2.80431520e-04 ... 2.64089357e-04
2.62781984e-04 2.57552492e-04]]
(376, 166800)
therefore
raw[:][0].shape[:][0] == raw.info['nchan']
True
and Raw[:][1] contains the corresponding time points
print(raw[:][1]) # 1 array containing times (1 × n_samps)
print(raw[:][1].shape)
[0.00000000e+00 1.66496011e-03 3.32992022e-03 ... 2.77710351e+02
2.77712016e+02 2.77713681e+02]
(166800,)
Although the above examples may be great for efficient programming pipelines, let’s be more specific to see what we are actually dealing with.
Let’s check out the channels named in the info object again.
raw.info['ch_names']
['MEG 0113',
'MEG 0112',
'MEG 0111',
'MEG 0122',
'MEG 0123',
'MEG 0121',
'MEG 0132',
'MEG 0133',
'MEG 0131',
'MEG 0143',
'MEG 0142',
'MEG 0141',
'MEG 0213',
'MEG 0212',
'MEG 0211',
'MEG 0222',
'MEG 0223',
'MEG 0221',
'MEG 0232',
'MEG 0233',
'MEG 0231',
'MEG 0243',
'MEG 0242',
'MEG 0241',
'MEG 0313',
'MEG 0312',
'MEG 0311',
'MEG 0322',
'MEG 0323',
'MEG 0321',
'MEG 0333',
'MEG 0332',
'MEG 0331',
'MEG 0343',
'MEG 0342',
'MEG 0341',
'MEG 0413',
'MEG 0412',
'MEG 0411',
'MEG 0422',
'MEG 0423',
'MEG 0421',
'MEG 0432',
'MEG 0433',
'MEG 0431',
'MEG 0443',
'MEG 0442',
'MEG 0441',
'MEG 0513',
'MEG 0512',
'MEG 0511',
'MEG 0523',
'MEG 0522',
'MEG 0521',
'MEG 0532',
'MEG 0533',
'MEG 0531',
'MEG 0542',
'MEG 0543',
'MEG 0541',
'MEG 0613',
'MEG 0612',
'MEG 0611',
'MEG 0622',
'MEG 0623',
'MEG 0621',
'MEG 0633',
'MEG 0632',
'MEG 0631',
'MEG 0642',
'MEG 0643',
'MEG 0641',
'MEG 0713',
'MEG 0712',
'MEG 0711',
'MEG 0723',
'MEG 0722',
'MEG 0721',
'MEG 0733',
'MEG 0732',
'MEG 0731',
'MEG 0743',
'MEG 0742',
'MEG 0741',
'MEG 0813',
'MEG 0812',
'MEG 0811',
'MEG 0822',
'MEG 0823',
'MEG 0821',
'MEG 0913',
'MEG 0912',
'MEG 0911',
'MEG 0923',
'MEG 0922',
'MEG 0921',
'MEG 0932',
'MEG 0933',
'MEG 0931',
'MEG 0942',
'MEG 0943',
'MEG 0941',
'MEG 1013',
'MEG 1012',
'MEG 1011',
'MEG 1023',
'MEG 1022',
'MEG 1021',
'MEG 1032',
'MEG 1033',
'MEG 1031',
'MEG 1043',
'MEG 1042',
'MEG 1041',
'MEG 1112',
'MEG 1113',
'MEG 1111',
'MEG 1123',
'MEG 1122',
'MEG 1121',
'MEG 1133',
'MEG 1132',
'MEG 1131',
'MEG 1142',
'MEG 1143',
'MEG 1141',
'MEG 1213',
'MEG 1212',
'MEG 1211',
'MEG 1223',
'MEG 1222',
'MEG 1221',
'MEG 1232',
'MEG 1233',
'MEG 1231',
'MEG 1243',
'MEG 1242',
'MEG 1241',
'MEG 1312',
'MEG 1313',
'MEG 1311',
'MEG 1323',
'MEG 1322',
'MEG 1321',
'MEG 1333',
'MEG 1332',
'MEG 1331',
'MEG 1342',
'MEG 1343',
'MEG 1341',
'MEG 1412',
'MEG 1413',
'MEG 1411',
'MEG 1423',
'MEG 1422',
'MEG 1421',
'MEG 1433',
'MEG 1432',
'MEG 1431',
'MEG 1442',
'MEG 1443',
'MEG 1441',
'MEG 1512',
'MEG 1513',
'MEG 1511',
'MEG 1522',
'MEG 1523',
'MEG 1521',
'MEG 1533',
'MEG 1532',
'MEG 1531',
'MEG 1543',
'MEG 1542',
'MEG 1541',
'MEG 1613',
'MEG 1612',
'MEG 1611',
'MEG 1622',
'MEG 1623',
'MEG 1621',
'MEG 1632',
'MEG 1633',
'MEG 1631',
'MEG 1643',
'MEG 1642',
'MEG 1641',
'MEG 1713',
'MEG 1712',
'MEG 1711',
'MEG 1722',
'MEG 1723',
'MEG 1721',
'MEG 1732',
'MEG 1733',
'MEG 1731',
'MEG 1743',
'MEG 1742',
'MEG 1741',
'MEG 1813',
'MEG 1812',
'MEG 1811',
'MEG 1822',
'MEG 1823',
'MEG 1821',
'MEG 1832',
'MEG 1833',
'MEG 1831',
'MEG 1843',
'MEG 1842',
'MEG 1841',
'MEG 1912',
'MEG 1913',
'MEG 1911',
'MEG 1923',
'MEG 1922',
'MEG 1921',
'MEG 1932',
'MEG 1933',
'MEG 1931',
'MEG 1943',
'MEG 1942',
'MEG 1941',
'MEG 2013',
'MEG 2012',
'MEG 2011',
'MEG 2023',
'MEG 2022',
'MEG 2021',
'MEG 2032',
'MEG 2033',
'MEG 2031',
'MEG 2042',
'MEG 2043',
'MEG 2041',
'MEG 2113',
'MEG 2112',
'MEG 2111',
'MEG 2122',
'MEG 2123',
'MEG 2121',
'MEG 2133',
'MEG 2132',
'MEG 2131',
'MEG 2143',
'MEG 2142',
'MEG 2141',
'MEG 2212',
'MEG 2213',
'MEG 2211',
'MEG 2223',
'MEG 2222',
'MEG 2221',
'MEG 2233',
'MEG 2232',
'MEG 2231',
'MEG 2242',
'MEG 2243',
'MEG 2241',
'MEG 2312',
'MEG 2313',
'MEG 2311',
'MEG 2323',
'MEG 2322',
'MEG 2321',
'MEG 2332',
'MEG 2333',
'MEG 2331',
'MEG 2343',
'MEG 2342',
'MEG 2341',
'MEG 2412',
'MEG 2413',
'MEG 2411',
'MEG 2423',
'MEG 2422',
'MEG 2421',
'MEG 2433',
'MEG 2432',
'MEG 2431',
'MEG 2442',
'MEG 2443',
'MEG 2441',
'MEG 2512',
'MEG 2513',
'MEG 2511',
'MEG 2522',
'MEG 2523',
'MEG 2521',
'MEG 2533',
'MEG 2532',
'MEG 2531',
'MEG 2543',
'MEG 2542',
'MEG 2541',
'MEG 2612',
'MEG 2613',
'MEG 2611',
'MEG 2623',
'MEG 2622',
'MEG 2621',
'MEG 2633',
'MEG 2632',
'MEG 2631',
'MEG 2642',
'MEG 2643',
'MEG 2641',
'STI 001',
'STI 002',
'STI 003',
'STI 004',
'STI 005',
'STI 006',
'STI 014',
'STI 015',
'STI 016',
'EEG 001',
'EEG 002',
'EEG 003',
'EEG 004',
'EEG 005',
'EEG 006',
'EEG 007',
'EEG 008',
'EEG 009',
'EEG 010',
'EEG 011',
'EEG 012',
'EEG 013',
'EEG 014',
'EEG 015',
'EEG 016',
'EEG 017',
'EEG 018',
'EEG 019',
'EEG 020',
'EEG 021',
'EEG 022',
'EEG 023',
'EEG 024',
'EEG 025',
'EEG 026',
'EEG 027',
'EEG 028',
'EEG 029',
'EEG 030',
'EEG 031',
'EEG 032',
'EEG 033',
'EEG 034',
'EEG 035',
'EEG 036',
'EEG 037',
'EEG 038',
'EEG 039',
'EEG 040',
'EEG 041',
'EEG 042',
'EEG 043',
'EEG 044',
'EEG 045',
'EEG 046',
'EEG 047',
'EEG 048',
'EEG 049',
'EEG 050',
'EEG 051',
'EEG 052',
'EEG 053',
'EEG 054',
'EEG 055',
'EEG 056',
'EEG 057',
'EEG 058',
'EEG 059',
'EEG 060',
'EOG 061']
using the ch_names list of our info object, we can access data explicitly by channel name
print(raw[['EEG 054'], :1000]) # access arrays for channel EEG 054 for the first 1000 samples
(array([[4.33922110e-05, 4.47882071e-05, 4.39738761e-05, 4.47300406e-05,
4.42647086e-05, 4.15890494e-05, 4.19380485e-05, 4.62423697e-05,
4.82200308e-05, 4.69403677e-05, 4.57770376e-05, 4.43228751e-05,
4.40902091e-05, 4.52535391e-05, 4.56025381e-05, 4.64168692e-05,
4.83945303e-05, 4.89761953e-05, 4.75220327e-05, 4.49627066e-05,
4.29850455e-05, 4.22288810e-05, 4.28687125e-05, 4.56607046e-05,
4.90343618e-05, 4.95578603e-05, 4.89761953e-05, 5.10120229e-05,
5.20008534e-05, 4.93251943e-05, 4.91506948e-05, 4.92088613e-05,
4.35667105e-05, 3.86225578e-05, 3.99022209e-05, 4.04257194e-05,
3.77500603e-05, 3.62958977e-05, 3.50162346e-05, 3.37947381e-05,
3.37365716e-05, 3.41437371e-05, 3.28640740e-05, 2.86179193e-05,
2.60004267e-05, 2.73382563e-05, 2.88505854e-05, 2.87924188e-05,
3.05374139e-05, 3.35620721e-05, 3.33875726e-05, 3.03047479e-05,
2.88505854e-05, 3.08864129e-05, 3.58887322e-05, 3.96695549e-05,
3.80408928e-05, 3.64703972e-05, 4.01930534e-05, 4.46137076e-05,
4.56025381e-05, 4.46137076e-05, 4.40902091e-05, 4.28105460e-05,
4.15890494e-05, 4.36830435e-05, 4.59515372e-05, 4.54862051e-05,
4.64168692e-05, 4.93251943e-05, 4.93251943e-05, 4.71148672e-05,
4.65913687e-05, 4.72893667e-05, 4.69403677e-05, 4.86271963e-05,
5.24080190e-05, 5.35131825e-05, 5.17681874e-05, 4.81618643e-05,
4.48463736e-05, 4.40902091e-05, 4.50790396e-05, 4.53698721e-05,
4.46137076e-05, 4.39157096e-05, 4.32758780e-05, 4.22288810e-05,
4.04838859e-05, 3.97277214e-05, 4.10073844e-05, 4.36248770e-05,
4.46718741e-05, 4.28105460e-05, 4.17053825e-05, 4.24615470e-05,
4.29268790e-05, 4.26360465e-05, 4.21125480e-05, 4.05420524e-05,
3.92623893e-05, 3.94950554e-05, 3.81572258e-05, 3.73428947e-05,
3.98440544e-05, 4.11237174e-05, 3.99603874e-05, 3.89133903e-05,
3.74010613e-05, 3.41437371e-05, 3.12354120e-05, 2.95485834e-05,
2.95485834e-05, 3.18752435e-05, 3.30385735e-05, 3.23405755e-05,
3.28640740e-05, 3.48999016e-05, 3.39110711e-05, 2.94904169e-05,
2.54769282e-05, 2.39645991e-05, 2.22196040e-05, 2.06491085e-05,
2.34992671e-05, 2.76872553e-05, 2.81525873e-05, 2.58259272e-05,
2.39645991e-05, 2.47207637e-05, 2.64657587e-05, 2.62330927e-05,
2.36156001e-05, 2.21032710e-05, 2.25104366e-05, 2.31502681e-05,
2.47789302e-05, 2.65820917e-05, 2.53024287e-05, 2.36737666e-05,
2.45462642e-05, 2.57095942e-05, 2.42554316e-05, 2.19869380e-05,
2.23359370e-05, 2.34411006e-05, 2.50697627e-05, 2.61167597e-05,
2.62912592e-05, 2.69892573e-05, 2.54769282e-05, 2.25686031e-05,
2.25104366e-05, 2.43717646e-05, 2.36737666e-05, 2.28012691e-05,
2.55932612e-05, 2.94904169e-05, 3.09445794e-05, 3.01884149e-05,
2.97230829e-05, 2.97812494e-05, 3.12935785e-05, 3.44345696e-05,
3.65867302e-05, 3.79245598e-05, 3.87970573e-05, 3.87388908e-05,
3.79827263e-05, 3.71683952e-05, 3.83898918e-05, 3.90878898e-05,
3.67612297e-05, 3.35620721e-05, 3.24569085e-05, 3.30385735e-05,
3.22824090e-05, 3.18752435e-05, 3.31549066e-05, 3.30967401e-05,
3.17007440e-05, 3.15262445e-05, 3.25150750e-05, 3.26314080e-05,
3.27477410e-05, 3.53070672e-05, 3.90297233e-05, 3.94950554e-05,
3.41437371e-05, 2.89087519e-05, 2.83270868e-05, 3.08282464e-05,
3.39692376e-05, 3.64122307e-05, 3.84480583e-05, 3.80990593e-05,
3.46672356e-05, 3.50162346e-05, 4.00185539e-05, 4.17635490e-05,
3.97277214e-05, 3.92042228e-05, 3.96113884e-05, 3.87970573e-05,
3.80990593e-05, 3.86225578e-05, 3.90297233e-05, 3.78663933e-05,
3.51907341e-05, 3.30967401e-05, 3.36202386e-05, 3.74592278e-05,
3.95532219e-05, 3.67612297e-05, 3.53652337e-05, 3.92623893e-05,
4.26360465e-05, 4.26360465e-05, 4.18217155e-05, 4.17053825e-05,
4.18798820e-05, 4.12400504e-05, 4.06583854e-05, 4.15308829e-05,
4.39157096e-05, 4.56025381e-05, 4.64750357e-05, 4.88016958e-05,
5.11283559e-05, 5.03140249e-05, 4.81036978e-05, 4.72312002e-05,
4.82781973e-05, 5.10120229e-05, 5.52581776e-05, 5.79920032e-05,
5.84573352e-05, 5.98533313e-05, 6.06676623e-05, 5.68868397e-05,
5.20590199e-05, 5.13610219e-05, 5.43856800e-05, 5.90390003e-05,
6.34596545e-05, 6.49138170e-05, 4.99068593e-05, 3.39692376e-05,
5.54908436e-05, 7.71869491e-05, 6.88109727e-05, 5.88063343e-05,
6.00278308e-05, 5.79920032e-05, 5.51418446e-05, 4.26360465e-05,
3.03629144e-05, 5.71776722e-05, 7.53837875e-05, 6.00859973e-05,
5.14773549e-05, 5.96206653e-05, 6.82874742e-05, 6.78803087e-05,
5.88645008e-05, 4.79873647e-05, 2.78617548e-05, 2.73964228e-05,
5.87481678e-05, 6.92763047e-05, 5.64796741e-05, 5.33968495e-05,
6.01441638e-05, 6.26453234e-05, 6.25289904e-05, 6.41576525e-05,
6.50883165e-05, 6.77058092e-05, 7.02651353e-05, 6.40994860e-05,
4.72893667e-05, 5.89808338e-05, 8.00371078e-05, 7.29989609e-05,
6.39831530e-05, 6.89854722e-05, 7.38132920e-05, 7.53256210e-05,
7.82921127e-05, 8.10841048e-05, 8.05024398e-05, 7.71287826e-05,
7.59654526e-05, 8.12004378e-05, 8.59119245e-05, 8.45159285e-05,
8.27709334e-05, 8.34689314e-05, 8.36434309e-05, 8.18984358e-05,
8.09677718e-05, 8.13749373e-05, 7.99207747e-05, 7.76522811e-05,
7.70706161e-05, 7.71287826e-05, 7.53837875e-05, 7.36387925e-05,
7.31152939e-05, 7.31152939e-05, 7.32897934e-05, 7.38132920e-05,
7.42786240e-05, 7.32897934e-05, 7.14866318e-05, 7.08468003e-05,
6.94508043e-05, 6.74149767e-05, 6.77639757e-05, 6.84619737e-05,
6.68914781e-05, 6.41576525e-05, 6.08421619e-05, 5.68286732e-05,
5.51418446e-05, 5.54908436e-05, 5.49091786e-05, 5.61888416e-05,
6.03768298e-05, 6.26453234e-05, 6.10748279e-05, 5.79920032e-05,
5.74103382e-05, 5.81083362e-05, 5.65960072e-05, 5.74685047e-05,
6.24126574e-05, 6.52628161e-05, 6.47393175e-05, 6.37504870e-05,
6.24708239e-05, 6.08421619e-05, 5.88063343e-05, 5.59561756e-05,
5.25825185e-05, 5.18263539e-05, 5.35713490e-05, 5.57235096e-05,
5.72940052e-05, 5.75266712e-05, 5.47346791e-05, 4.77546987e-05,
4.08328849e-05, 4.20543815e-05, 4.60097037e-05, 4.39738761e-05,
3.96113884e-05, 3.65285637e-05, 3.38529046e-05, 3.23987420e-05,
3.25150750e-05, 3.32130731e-05, 3.37947381e-05, 3.41437371e-05,
3.49580681e-05, 3.55397332e-05, 3.69357292e-05, 4.04838859e-05,
4.49627066e-05, 4.70567007e-05, 4.75220327e-05, 4.79291982e-05,
4.80455313e-05, 4.83363638e-05, 4.84526968e-05, 4.85108633e-05,
4.89180288e-05, 4.88598623e-05, 4.79873647e-05, 4.72893667e-05,
4.70567007e-05, 4.76383657e-05, 4.91506948e-05, 5.06048574e-05,
5.24661855e-05, 5.34550160e-05, 5.20590199e-05, 5.25825185e-05,
5.68286732e-05, 6.00278308e-05, 6.07839953e-05, 6.22963244e-05,
6.58444811e-05, 6.95089708e-05, 7.15447984e-05, 7.21846299e-05,
7.13702988e-05, 7.06141343e-05, 7.07886338e-05, 7.00324693e-05,
6.86946397e-05, 6.80548082e-05, 6.75313097e-05, 6.68914781e-05,
6.67169786e-05, 6.67751451e-05, 6.49719835e-05, 6.17728259e-05,
5.88063343e-05, 5.56653431e-05, 5.35131825e-05, 5.42111805e-05,
5.42693470e-05, 5.00813588e-05, 4.57188711e-05, 4.55443716e-05,
4.67077017e-05, 4.40902091e-05, 3.97277214e-05, 3.84480583e-05,
3.93205558e-05, 3.92042228e-05, 3.65285637e-05, 3.37365716e-05,
3.32130731e-05, 3.32130731e-05, 3.37947381e-05, 3.53652337e-05,
3.60632317e-05, 3.42600701e-05, 3.38529046e-05, 3.75755608e-05,
4.12982169e-05, 4.36830435e-05, 4.74056997e-05, 5.21171864e-05,
5.32223500e-05, 5.21753529e-05, 5.39785145e-05, 5.51418446e-05,
5.33968495e-05, 5.35713490e-05, 5.56071766e-05, 5.68286732e-05,
5.72940052e-05, 5.63633411e-05, 5.39203480e-05, 5.25243520e-05,
5.33386830e-05, 5.46183461e-05, 5.56071766e-05, 5.65960072e-05,
5.65378406e-05, 5.53745106e-05, 5.32805165e-05, 5.00813588e-05,
4.67658682e-05, 4.43228751e-05, 4.25197135e-05, 4.04257194e-05,
4.01348869e-05, 4.11237174e-05, 4.08910514e-05, 3.97858879e-05,
3.83317253e-05, 3.69938957e-05, 3.68775627e-05, 3.57142327e-05,
3.27477410e-05, 3.19915765e-05, 3.40274041e-05, 3.46090691e-05,
3.19915765e-05, 3.15262445e-05, 3.68193962e-05, 4.38575431e-05,
4.69403677e-05, 4.93833608e-05, 5.53745106e-05, 6.31688220e-05,
6.87528062e-05, 7.10794663e-05, 7.20101304e-05, 7.55001205e-05,
7.95717757e-05, 8.00952743e-05, 8.09677718e-05, 8.21892684e-05,
8.34107649e-05, 8.48067610e-05, 8.55629255e-05, 8.54465925e-05,
8.25964339e-05, 8.00952743e-05, 8.12586043e-05, 8.21892684e-05,
8.11422713e-05, 8.04442733e-05, 7.84084457e-05, 7.44531235e-05,
7.39877915e-05, 7.49184555e-05, 7.36969590e-05, 7.36387925e-05,
7.53256210e-05, 7.68379501e-05, 7.64889511e-05, 7.43367905e-05,
7.42204575e-05, 7.57327865e-05, 7.73032821e-05, 7.86992782e-05,
7.96299422e-05, 8.02116073e-05, 7.96881087e-05, 7.74777816e-05,
7.55001205e-05, 7.48021225e-05, 7.28826279e-05, 7.05559678e-05,
6.95089708e-05, 6.74149767e-05, 6.44484850e-05, 6.07258288e-05,
5.83410022e-05, 6.07258288e-05, 6.32269885e-05, 6.16564929e-05,
6.00278308e-05, 6.10748279e-05, 6.00859973e-05, 5.87481678e-05,
6.17146594e-05, 6.36923205e-05, 6.18891589e-05, 6.15983264e-05,
6.39249865e-05, 6.55536486e-05, 6.56699816e-05, 6.54373156e-05,
6.59608141e-05, 6.61934801e-05, 6.40994860e-05, 6.40994860e-05,
6.59026476e-05, 6.59608141e-05, 6.49138170e-05, 6.52628161e-05,
6.64261461e-05, 6.60189806e-05, 6.47393175e-05, 6.48556505e-05,
6.43321520e-05, 6.26453234e-05, 6.23544909e-05, 6.37504870e-05,
6.43903185e-05, 6.38086535e-05, 6.34596545e-05, 6.27616564e-05,
6.21218249e-05, 6.24708239e-05, 6.14819934e-05, 5.74685047e-05,
5.63051746e-05, 5.93879993e-05, 5.87481678e-05, 5.35131825e-05,
5.03721914e-05, 5.06630239e-05, 5.01976919e-05, 4.83945303e-05,
4.67077017e-05, 4.57770376e-05, 4.60097037e-05, 4.69403677e-05,
4.70567007e-05, 4.54862051e-05, 4.43228751e-05, 4.60678702e-05,
4.76965322e-05, 4.85108633e-05, 4.92088613e-05, 4.97323598e-05,
5.06048574e-05, 5.10120229e-05, 5.28733510e-05, 5.45020131e-05,
5.33386830e-05, 5.17100209e-05, 5.06048574e-05, 5.10701894e-05,
5.19426869e-05, 5.22916860e-05, 5.20008534e-05, 4.97323598e-05,
4.73475332e-05, 4.76383657e-05, 4.82781973e-05, 4.82781973e-05,
4.83363638e-05, 4.90925283e-05, 5.07211904e-05, 5.31060170e-05,
5.58398426e-05, 5.75266712e-05, 5.74685047e-05, 5.70613392e-05,
5.62470081e-05, 5.64796741e-05, 5.69450062e-05, 5.60143421e-05,
5.66541737e-05, 5.92134998e-05, 5.97951648e-05, 5.78175037e-05,
5.58980091e-05, 5.69450062e-05, 5.89808338e-05, 5.82828357e-05,
5.70031727e-05, 5.76430042e-05, 5.80501697e-05, 5.68286732e-05,
5.63051746e-05, 5.78756702e-05, 5.93879993e-05, 5.90971668e-05,
5.93298328e-05, 6.14238269e-05, 6.14819934e-05, 6.12493274e-05,
6.26453234e-05, 6.29361559e-05, 6.04931628e-05, 5.79920032e-05,
5.64796741e-05, 5.69450062e-05, 6.10166614e-05, 6.33433215e-05,
6.22963244e-05, 6.27034899e-05, 6.55536486e-05, 6.59026476e-05,
6.26453234e-05, 6.09003284e-05, 6.21218249e-05, 6.46811510e-05,
6.75313097e-05, 6.99743028e-05, 7.17774644e-05, 7.23009629e-05,
7.28244614e-05, 7.28826279e-05, 6.95671373e-05, 6.60189806e-05,
6.50301500e-05, 6.60771471e-05, 6.91599717e-05, 6.91018052e-05,
6.52046496e-05, 6.42158190e-05, 6.50301500e-05, 6.38086535e-05,
6.20636584e-05, 6.26453234e-05, 6.31106555e-05, 6.04931628e-05,
5.75848377e-05, 5.77011707e-05, 5.82246692e-05, 5.78756702e-05,
5.68286732e-05, 5.65378406e-05, 5.73521717e-05, 5.62470081e-05,
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1.17379688, 1.17546184, 1.1771268 , 1.17879176, 1.18045672,
1.18212168, 1.18378664, 1.1854516 , 1.18711656, 1.18878152,
1.19044648, 1.19211144, 1.1937764 , 1.19544136, 1.19710632,
1.19877128, 1.20043624, 1.2021012 , 1.20376616, 1.20543112,
1.20709608, 1.20876104, 1.210426 , 1.21209096, 1.21375592,
1.21542088, 1.21708584, 1.2187508 , 1.22041576, 1.22208072,
1.22374568, 1.22541064, 1.2270756 , 1.22874056, 1.23040552,
1.23207048, 1.23373544, 1.2354004 , 1.23706536, 1.23873032,
1.24039528, 1.24206024, 1.2437252 , 1.24539016, 1.24705512,
1.24872008, 1.25038504, 1.25205 , 1.25371496, 1.25537992,
1.25704488, 1.25870984, 1.2603748 , 1.26203976, 1.26370472,
1.26536968, 1.26703464, 1.2686996 , 1.27036456, 1.27202952,
1.27369448, 1.27535944, 1.2770244 , 1.27868936, 1.28035432,
1.28201928, 1.28368424, 1.2853492 , 1.28701416, 1.28867912,
1.29034408, 1.29200905, 1.29367401, 1.29533897, 1.29700393,
1.29866889, 1.30033385, 1.30199881, 1.30366377, 1.30532873,
1.30699369, 1.30865865, 1.31032361, 1.31198857, 1.31365353,
1.31531849, 1.31698345, 1.31864841, 1.32031337, 1.32197833,
1.32364329, 1.32530825, 1.32697321, 1.32863817, 1.33030313,
1.33196809, 1.33363305, 1.33529801, 1.33696297, 1.33862793,
1.34029289, 1.34195785, 1.34362281, 1.34528777, 1.34695273,
1.34861769, 1.35028265, 1.35194761, 1.35361257, 1.35527753,
1.35694249, 1.35860745, 1.36027241, 1.36193737, 1.36360233,
1.36526729, 1.36693225, 1.36859721, 1.37026217, 1.37192713,
1.37359209, 1.37525705, 1.37692201, 1.37858697, 1.38025193,
1.38191689, 1.38358185, 1.38524681, 1.38691177, 1.38857673,
1.39024169, 1.39190665, 1.39357161, 1.39523657, 1.39690153,
1.39856649, 1.40023145, 1.40189641, 1.40356137, 1.40522633,
1.40689129, 1.40855625, 1.41022121, 1.41188617, 1.41355113,
1.41521609, 1.41688105, 1.41854601, 1.42021097, 1.42187593,
1.42354089, 1.42520585, 1.42687081, 1.42853577, 1.43020073,
1.43186569, 1.43353065, 1.43519561, 1.43686057, 1.43852553,
1.44019049, 1.44185545, 1.44352042, 1.44518538, 1.44685034,
1.4485153 , 1.45018026, 1.45184522, 1.45351018, 1.45517514,
1.4568401 , 1.45850506, 1.46017002, 1.46183498, 1.46349994,
1.4651649 , 1.46682986, 1.46849482, 1.47015978, 1.47182474,
1.4734897 , 1.47515466, 1.47681962, 1.47848458, 1.48014954,
1.4818145 , 1.48347946, 1.48514442, 1.48680938, 1.48847434,
1.4901393 , 1.49180426, 1.49346922, 1.49513418, 1.49679914,
1.4984641 , 1.50012906, 1.50179402, 1.50345898, 1.50512394,
1.5067889 , 1.50845386, 1.51011882, 1.51178378, 1.51344874,
1.5151137 , 1.51677866, 1.51844362, 1.52010858, 1.52177354,
1.5234385 , 1.52510346, 1.52676842, 1.52843338, 1.53009834,
1.5317633 , 1.53342826, 1.53509322, 1.53675818, 1.53842314,
1.5400881 , 1.54175306, 1.54341802, 1.54508298, 1.54674794,
1.5484129 , 1.55007786, 1.55174282, 1.55340778, 1.55507274,
1.5567377 , 1.55840266, 1.56006762, 1.56173258, 1.56339754,
1.5650625 , 1.56672746, 1.56839242, 1.57005738, 1.57172234,
1.5733873 , 1.57505226, 1.57671722, 1.57838218, 1.58004714,
1.5817121 , 1.58337706, 1.58504202, 1.58670698, 1.58837194,
1.5900369 , 1.59170186, 1.59336682, 1.59503179, 1.59669675,
1.59836171, 1.60002667, 1.60169163, 1.60335659, 1.60502155,
1.60668651, 1.60835147, 1.61001643, 1.61168139, 1.61334635,
1.61501131, 1.61667627, 1.61834123, 1.62000619, 1.62167115,
1.62333611, 1.62500107, 1.62666603, 1.62833099, 1.62999595,
1.63166091, 1.63332587, 1.63499083, 1.63665579, 1.63832075,
1.63998571, 1.64165067, 1.64331563, 1.64498059, 1.64664555,
1.64831051, 1.64997547, 1.65164043, 1.65330539, 1.65497035,
1.65663531, 1.65830027, 1.65996523, 1.66163019, 1.66329515]))
or for multiple channels.
print(raw[['EEG 054', 'EEG 055'], 1000:2000])
(array([[4.68822012e-05, 4.50790396e-05, 4.54280386e-05, ...,
4.27523795e-05, 3.99022209e-05, 3.66448967e-05],
[6.31586928e-05, 6.23315444e-05, 6.17407241e-05, ...,
5.53598645e-05, 5.30556653e-05, 5.00424816e-05]]), array([1.66496011, 1.66662507, 1.66829003, 1.66995499, 1.67161995,
1.67328491, 1.67494987, 1.67661483, 1.67827979, 1.67994475,
1.68160971, 1.68327467, 1.68493963, 1.68660459, 1.68826955,
1.68993451, 1.69159947, 1.69326443, 1.69492939, 1.69659435,
1.69825931, 1.69992427, 1.70158923, 1.70325419, 1.70491915,
1.70658411, 1.70824907, 1.70991403, 1.71157899, 1.71324395,
1.71490891, 1.71657387, 1.71823883, 1.71990379, 1.72156875,
1.72323371, 1.72489867, 1.72656363, 1.72822859, 1.72989355,
1.73155851, 1.73322347, 1.73488843, 1.73655339, 1.73821835,
1.73988331, 1.74154827, 1.74321323, 1.74487819, 1.74654316,
1.74820812, 1.74987308, 1.75153804, 1.753203 , 1.75486796,
1.75653292, 1.75819788, 1.75986284, 1.7615278 , 1.76319276,
1.76485772, 1.76652268, 1.76818764, 1.7698526 , 1.77151756,
1.77318252, 1.77484748, 1.77651244, 1.7781774 , 1.77984236,
1.78150732, 1.78317228, 1.78483724, 1.7865022 , 1.78816716,
1.78983212, 1.79149708, 1.79316204, 1.794827 , 1.79649196,
1.79815692, 1.79982188, 1.80148684, 1.8031518 , 1.80481676,
1.80648172, 1.80814668, 1.80981164, 1.8114766 , 1.81314156,
1.81480652, 1.81647148, 1.81813644, 1.8198014 , 1.82146636,
1.82313132, 1.82479628, 1.82646124, 1.8281262 , 1.82979116,
1.83145612, 1.83312108, 1.83478604, 1.836451 , 1.83811596,
1.83978092, 1.84144588, 1.84311084, 1.8447758 , 1.84644076,
1.84810572, 1.84977068, 1.85143564, 1.8531006 , 1.85476556,
1.85643052, 1.85809548, 1.85976044, 1.8614254 , 1.86309036,
1.86475532, 1.86642028, 1.86808524, 1.8697502 , 1.87141516,
1.87308012, 1.87474508, 1.87641004, 1.878075 , 1.87973996,
1.88140492, 1.88306988, 1.88473484, 1.8863998 , 1.88806476,
1.88972972, 1.89139468, 1.89305964, 1.8947246 , 1.89638956,
1.89805453, 1.89971949, 1.90138445, 1.90304941, 1.90471437,
1.90637933, 1.90804429, 1.90970925, 1.91137421, 1.91303917,
1.91470413, 1.91636909, 1.91803405, 1.91969901, 1.92136397,
1.92302893, 1.92469389, 1.92635885, 1.92802381, 1.92968877,
1.93135373, 1.93301869, 1.93468365, 1.93634861, 1.93801357,
1.93967853, 1.94134349, 1.94300845, 1.94467341, 1.94633837,
1.94800333, 1.94966829, 1.95133325, 1.95299821, 1.95466317,
1.95632813, 1.95799309, 1.95965805, 1.96132301, 1.96298797,
1.96465293, 1.96631789, 1.96798285, 1.96964781, 1.97131277,
1.97297773, 1.97464269, 1.97630765, 1.97797261, 1.97963757,
1.98130253, 1.98296749, 1.98463245, 1.98629741, 1.98796237,
1.98962733, 1.99129229, 1.99295725, 1.99462221, 1.99628717,
1.99795213, 1.99961709, 2.00128205, 2.00294701, 2.00461197,
2.00627693, 2.00794189, 2.00960685, 2.01127181, 2.01293677,
2.01460173, 2.01626669, 2.01793165, 2.01959661, 2.02126157,
2.02292653, 2.02459149, 2.02625645, 2.02792141, 2.02958637,
2.03125133, 2.03291629, 2.03458125, 2.03624621, 2.03791117,
2.03957613, 2.04124109, 2.04290605, 2.04457101, 2.04623597,
2.04790093, 2.04956589, 2.05123086, 2.05289582, 2.05456078,
2.05622574, 2.0578907 , 2.05955566, 2.06122062, 2.06288558,
2.06455054, 2.0662155 , 2.06788046, 2.06954542, 2.07121038,
2.07287534, 2.0745403 , 2.07620526, 2.07787022, 2.07953518,
2.08120014, 2.0828651 , 2.08453006, 2.08619502, 2.08785998,
2.08952494, 2.0911899 , 2.09285486, 2.09451982, 2.09618478,
2.09784974, 2.0995147 , 2.10117966, 2.10284462, 2.10450958,
2.10617454, 2.1078395 , 2.10950446, 2.11116942, 2.11283438,
2.11449934, 2.1161643 , 2.11782926, 2.11949422, 2.12115918,
2.12282414, 2.1244891 , 2.12615406, 2.12781902, 2.12948398,
2.13114894, 2.1328139 , 2.13447886, 2.13614382, 2.13780878,
2.13947374, 2.1411387 , 2.14280366, 2.14446862, 2.14613358,
2.14779854, 2.1494635 , 2.15112846, 2.15279342, 2.15445838,
2.15612334, 2.1577883 , 2.15945326, 2.16111822, 2.16278318,
2.16444814, 2.1661131 , 2.16777806, 2.16944302, 2.17110798,
2.17277294, 2.1744379 , 2.17610286, 2.17776782, 2.17943278,
2.18109774, 2.1827627 , 2.18442766, 2.18609262, 2.18775758,
2.18942254, 2.1910875 , 2.19275246, 2.19441742, 2.19608238,
2.19774734, 2.1994123 , 2.20107726, 2.20274223, 2.20440719,
2.20607215, 2.20773711, 2.20940207, 2.21106703, 2.21273199,
2.21439695, 2.21606191, 2.21772687, 2.21939183, 2.22105679,
2.22272175, 2.22438671, 2.22605167, 2.22771663, 2.22938159,
2.23104655, 2.23271151, 2.23437647, 2.23604143, 2.23770639,
2.23937135, 2.24103631, 2.24270127, 2.24436623, 2.24603119,
2.24769615, 2.24936111, 2.25102607, 2.25269103, 2.25435599,
2.25602095, 2.25768591, 2.25935087, 2.26101583, 2.26268079,
2.26434575, 2.26601071, 2.26767567, 2.26934063, 2.27100559,
2.27267055, 2.27433551, 2.27600047, 2.27766543, 2.27933039,
2.28099535, 2.28266031, 2.28432527, 2.28599023, 2.28765519,
2.28932015, 2.29098511, 2.29265007, 2.29431503, 2.29597999,
2.29764495, 2.29930991, 2.30097487, 2.30263983, 2.30430479,
2.30596975, 2.30763471, 2.30929967, 2.31096463, 2.31262959,
2.31429455, 2.31595951, 2.31762447, 2.31928943, 2.32095439,
2.32261935, 2.32428431, 2.32594927, 2.32761423, 2.32927919,
2.33094415, 2.33260911, 2.33427407, 2.33593903, 2.33760399,
2.33926895, 2.34093391, 2.34259887, 2.34426383, 2.34592879,
2.34759375, 2.34925871, 2.35092367, 2.35258863, 2.3542536 ,
2.35591856, 2.35758352, 2.35924848, 2.36091344, 2.3625784 ,
2.36424336, 2.36590832, 2.36757328, 2.36923824, 2.3709032 ,
2.37256816, 2.37423312, 2.37589808, 2.37756304, 2.379228 ,
2.38089296, 2.38255792, 2.38422288, 2.38588784, 2.3875528 ,
2.38921776, 2.39088272, 2.39254768, 2.39421264, 2.3958776 ,
2.39754256, 2.39920752, 2.40087248, 2.40253744, 2.4042024 ,
2.40586736, 2.40753232, 2.40919728, 2.41086224, 2.4125272 ,
2.41419216, 2.41585712, 2.41752208, 2.41918704, 2.420852 ,
2.42251696, 2.42418192, 2.42584688, 2.42751184, 2.4291768 ,
2.43084176, 2.43250672, 2.43417168, 2.43583664, 2.4375016 ,
2.43916656, 2.44083152, 2.44249648, 2.44416144, 2.4458264 ,
2.44749136, 2.44915632, 2.45082128, 2.45248624, 2.4541512 ,
2.45581616, 2.45748112, 2.45914608, 2.46081104, 2.462476 ,
2.46414096, 2.46580592, 2.46747088, 2.46913584, 2.4708008 ,
2.47246576, 2.47413072, 2.47579568, 2.47746064, 2.4791256 ,
2.48079056, 2.48245552, 2.48412048, 2.48578544, 2.4874504 ,
2.48911536, 2.49078032, 2.49244528, 2.49411024, 2.4957752 ,
2.49744016, 2.49910512, 2.50077008, 2.50243504, 2.5041 ,
2.50576497, 2.50742993, 2.50909489, 2.51075985, 2.51242481,
2.51408977, 2.51575473, 2.51741969, 2.51908465, 2.52074961,
2.52241457, 2.52407953, 2.52574449, 2.52740945, 2.52907441,
2.53073937, 2.53240433, 2.53406929, 2.53573425, 2.53739921,
2.53906417, 2.54072913, 2.54239409, 2.54405905, 2.54572401,
2.54738897, 2.54905393, 2.55071889, 2.55238385, 2.55404881,
2.55571377, 2.55737873, 2.55904369, 2.56070865, 2.56237361,
2.56403857, 2.56570353, 2.56736849, 2.56903345, 2.57069841,
2.57236337, 2.57402833, 2.57569329, 2.57735825, 2.57902321,
2.58068817, 2.58235313, 2.58401809, 2.58568305, 2.58734801,
2.58901297, 2.59067793, 2.59234289, 2.59400785, 2.59567281,
2.59733777, 2.59900273, 2.60066769, 2.60233265, 2.60399761,
2.60566257, 2.60732753, 2.60899249, 2.61065745, 2.61232241,
2.61398737, 2.61565233, 2.61731729, 2.61898225, 2.62064721,
2.62231217, 2.62397713, 2.62564209, 2.62730705, 2.62897201,
2.63063697, 2.63230193, 2.63396689, 2.63563185, 2.63729681,
2.63896177, 2.64062673, 2.64229169, 2.64395665, 2.64562161,
2.64728657, 2.64895153, 2.65061649, 2.65228145, 2.65394641,
2.65561137, 2.65727634, 2.6589413 , 2.66060626, 2.66227122,
2.66393618, 2.66560114, 2.6672661 , 2.66893106, 2.67059602,
2.67226098, 2.67392594, 2.6755909 , 2.67725586, 2.67892082,
2.68058578, 2.68225074, 2.6839157 , 2.68558066, 2.68724562,
2.68891058, 2.69057554, 2.6922405 , 2.69390546, 2.69557042,
2.69723538, 2.69890034, 2.7005653 , 2.70223026, 2.70389522,
2.70556018, 2.70722514, 2.7088901 , 2.71055506, 2.71222002,
2.71388498, 2.71554994, 2.7172149 , 2.71887986, 2.72054482,
2.72220978, 2.72387474, 2.7255397 , 2.72720466, 2.72886962,
2.73053458, 2.73219954, 2.7338645 , 2.73552946, 2.73719442,
2.73885938, 2.74052434, 2.7421893 , 2.74385426, 2.74551922,
2.74718418, 2.74884914, 2.7505141 , 2.75217906, 2.75384402,
2.75550898, 2.75717394, 2.7588389 , 2.76050386, 2.76216882,
2.76383378, 2.76549874, 2.7671637 , 2.76882866, 2.77049362,
2.77215858, 2.77382354, 2.7754885 , 2.77715346, 2.77881842,
2.78048338, 2.78214834, 2.7838133 , 2.78547826, 2.78714322,
2.78880818, 2.79047314, 2.7921381 , 2.79380306, 2.79546802,
2.79713298, 2.79879794, 2.8004629 , 2.80212786, 2.80379282,
2.80545778, 2.80712274, 2.8087877 , 2.81045267, 2.81211763,
2.81378259, 2.81544755, 2.81711251, 2.81877747, 2.82044243,
2.82210739, 2.82377235, 2.82543731, 2.82710227, 2.82876723,
2.83043219, 2.83209715, 2.83376211, 2.83542707, 2.83709203,
2.83875699, 2.84042195, 2.84208691, 2.84375187, 2.84541683,
2.84708179, 2.84874675, 2.85041171, 2.85207667, 2.85374163,
2.85540659, 2.85707155, 2.85873651, 2.86040147, 2.86206643,
2.86373139, 2.86539635, 2.86706131, 2.86872627, 2.87039123,
2.87205619, 2.87372115, 2.87538611, 2.87705107, 2.87871603,
2.88038099, 2.88204595, 2.88371091, 2.88537587, 2.88704083,
2.88870579, 2.89037075, 2.89203571, 2.89370067, 2.89536563,
2.89703059, 2.89869555, 2.90036051, 2.90202547, 2.90369043,
2.90535539, 2.90702035, 2.90868531, 2.91035027, 2.91201523,
2.91368019, 2.91534515, 2.91701011, 2.91867507, 2.92034003,
2.92200499, 2.92366995, 2.92533491, 2.92699987, 2.92866483,
2.93032979, 2.93199475, 2.93365971, 2.93532467, 2.93698963,
2.93865459, 2.94031955, 2.94198451, 2.94364947, 2.94531443,
2.94697939, 2.94864435, 2.95030931, 2.95197427, 2.95363923,
2.95530419, 2.95696915, 2.95863411, 2.96029907, 2.96196404,
2.963629 , 2.96529396, 2.96695892, 2.96862388, 2.97028884,
2.9719538 , 2.97361876, 2.97528372, 2.97694868, 2.97861364,
2.9802786 , 2.98194356, 2.98360852, 2.98527348, 2.98693844,
2.9886034 , 2.99026836, 2.99193332, 2.99359828, 2.99526324,
2.9969282 , 2.99859316, 3.00025812, 3.00192308, 3.00358804,
3.005253 , 3.00691796, 3.00858292, 3.01024788, 3.01191284,
3.0135778 , 3.01524276, 3.01690772, 3.01857268, 3.02023764,
3.0219026 , 3.02356756, 3.02523252, 3.02689748, 3.02856244,
3.0302274 , 3.03189236, 3.03355732, 3.03522228, 3.03688724,
3.0385522 , 3.04021716, 3.04188212, 3.04354708, 3.04521204,
3.046877 , 3.04854196, 3.05020692, 3.05187188, 3.05353684,
3.0552018 , 3.05686676, 3.05853172, 3.06019668, 3.06186164,
3.0635266 , 3.06519156, 3.06685652, 3.06852148, 3.07018644,
3.0718514 , 3.07351636, 3.07518132, 3.07684628, 3.07851124,
3.0801762 , 3.08184116, 3.08350612, 3.08517108, 3.08683604,
3.088501 , 3.09016596, 3.09183092, 3.09349588, 3.09516084,
3.0968258 , 3.09849076, 3.10015572, 3.10182068, 3.10348564,
3.1051506 , 3.10681556, 3.10848052, 3.11014548, 3.11181044,
3.11347541, 3.11514037, 3.11680533, 3.11847029, 3.12013525,
3.12180021, 3.12346517, 3.12513013, 3.12679509, 3.12846005,
3.13012501, 3.13178997, 3.13345493, 3.13511989, 3.13678485,
3.13844981, 3.14011477, 3.14177973, 3.14344469, 3.14510965,
3.14677461, 3.14843957, 3.15010453, 3.15176949, 3.15343445,
3.15509941, 3.15676437, 3.15842933, 3.16009429, 3.16175925,
3.16342421, 3.16508917, 3.16675413, 3.16841909, 3.17008405,
3.17174901, 3.17341397, 3.17507893, 3.17674389, 3.17840885,
3.18007381, 3.18173877, 3.18340373, 3.18506869, 3.18673365,
3.18839861, 3.19006357, 3.19172853, 3.19339349, 3.19505845,
3.19672341, 3.19838837, 3.20005333, 3.20171829, 3.20338325,
3.20504821, 3.20671317, 3.20837813, 3.21004309, 3.21170805,
3.21337301, 3.21503797, 3.21670293, 3.21836789, 3.22003285,
3.22169781, 3.22336277, 3.22502773, 3.22669269, 3.22835765,
3.23002261, 3.23168757, 3.23335253, 3.23501749, 3.23668245,
3.23834741, 3.24001237, 3.24167733, 3.24334229, 3.24500725,
3.24667221, 3.24833717, 3.25000213, 3.25166709, 3.25333205,
3.25499701, 3.25666197, 3.25832693, 3.25999189, 3.26165685,
3.26332181, 3.26498678, 3.26665174, 3.2683167 , 3.26998166,
3.27164662, 3.27331158, 3.27497654, 3.2766415 , 3.27830646,
3.27997142, 3.28163638, 3.28330134, 3.2849663 , 3.28663126,
3.28829622, 3.28996118, 3.29162614, 3.2932911 , 3.29495606,
3.29662102, 3.29828598, 3.29995094, 3.3016159 , 3.30328086,
3.30494582, 3.30661078, 3.30827574, 3.3099407 , 3.31160566,
3.31327062, 3.31493558, 3.31660054, 3.3182655 , 3.31993046,
3.32159542, 3.32326038, 3.32492534, 3.3265903 , 3.32825526]))
Referencing specific arrays containing our amplitudes corresponding to the respective channels can again be achieved via tuple indexing
print(raw[['EEG 054', 'EEG 055'], 1000:2000][0][0]) # access data in channel 'EEG 054' for sample 1000 to 2000
[4.68822012e-05 4.50790396e-05 4.54280386e-05 4.77546987e-05
4.80455313e-05 4.66495352e-05 4.54862051e-05 4.72893667e-05
5.11865224e-05 5.16518544e-05 5.00813588e-05 4.96160268e-05
4.85690298e-05 4.60678702e-05 4.49627066e-05 4.51372061e-05
4.68822012e-05 4.94415273e-05 5.03721914e-05 4.92670278e-05
4.97323598e-05 5.13028554e-05 5.20008534e-05 5.31641835e-05
5.32223500e-05 5.05466909e-05 4.89180288e-05 4.96741933e-05
5.06048574e-05 5.10701894e-05 5.26406850e-05 5.48510121e-05
5.49673451e-05 5.42111805e-05 5.43856800e-05 5.38621815e-05
5.32223500e-05 5.27570180e-05 5.22916860e-05 5.13028554e-05
5.26406850e-05 5.51418446e-05 5.54326771e-05 5.47928456e-05
5.53163441e-05 5.58398426e-05 5.60725086e-05 5.57235096e-05
5.57816761e-05 5.77011707e-05 6.03186633e-05 6.00278308e-05
5.83410022e-05 5.99696643e-05 6.15401599e-05 5.81665027e-05
5.36876820e-05 5.07793569e-05 4.90343618e-05 4.92088613e-05
5.00231923e-05 5.08956899e-05 5.31060170e-05 5.25825185e-05
4.76965322e-05 4.54862051e-05 4.84526968e-05 5.09538564e-05
4.89180288e-05 4.55443716e-05 4.53117056e-05 4.49627066e-05
4.28105460e-05 4.10073844e-05 4.18217155e-05 4.39157096e-05
4.57770376e-05 4.61260367e-05 4.47882071e-05 4.39738761e-05
4.60678702e-05 5.01976919e-05 5.26406850e-05 5.13028554e-05
4.71730337e-05 4.30432120e-05 4.13563834e-05 4.24615470e-05
4.54862051e-05 4.75801992e-05 4.64168692e-05 4.37993766e-05
4.19962150e-05 4.17635490e-05 4.12400504e-05 4.07165519e-05
4.21125480e-05 4.44392081e-05 4.46718741e-05 4.20543815e-05
3.80990593e-05 3.73428947e-05 3.97858879e-05 4.00767204e-05
3.93205558e-05 4.04838859e-05 4.17635490e-05 4.12400504e-05
4.04257194e-05 3.94950554e-05 3.94950554e-05 4.06583854e-05
3.88552238e-05 3.57142327e-05 3.51325676e-05 3.57142327e-05
3.68193962e-05 3.69357292e-05 3.50162346e-05 3.51325676e-05
3.67030632e-05 3.57723992e-05 3.48417351e-05 3.72265617e-05
4.22870475e-05 4.74056997e-05 5.04885244e-05 5.22916860e-05
5.60725086e-05 6.15983264e-05 6.49138170e-05 6.50301500e-05
6.31106555e-05 6.21799914e-05 6.19473254e-05 6.04349963e-05
6.07258288e-05 6.23544909e-05 6.12493274e-05 5.87481678e-05
5.78756702e-05 5.99114978e-05 6.21799914e-05 6.25289904e-05
6.08421619e-05 5.79338367e-05 5.58980091e-05 5.58980091e-05
5.72940052e-05 5.65960072e-05 5.48510121e-05 5.52000111e-05
5.64796741e-05 5.49673451e-05 5.29315175e-05 5.46183461e-05
5.75266712e-05 5.82246692e-05 5.72940052e-05 5.60143421e-05
5.54326771e-05 5.56653431e-05 5.61888416e-05 5.65378406e-05
5.46765126e-05 5.15936879e-05 5.07793569e-05 5.24661855e-05
5.31641835e-05 5.26406850e-05 5.17681874e-05 5.09538564e-05
5.12446889e-05 5.25825185e-05 5.38621815e-05 5.38040150e-05
5.44438466e-05 5.52000111e-05 5.54908436e-05 5.57235096e-05
5.59561756e-05 5.66541737e-05 5.47928456e-05 5.07211904e-05
5.07211904e-05 5.39203480e-05 5.56653431e-05 5.54326771e-05
5.84573352e-05 6.37504870e-05 6.34014880e-05 5.88063343e-05
5.59561756e-05 5.71776722e-05 5.86318347e-05 5.74103382e-05
5.57235096e-05 5.49673451e-05 5.25243520e-05 4.84526968e-05
4.75801992e-05 5.07793569e-05 5.14191884e-05 5.04303579e-05
5.18263539e-05 5.32223500e-05 5.26988515e-05 5.11283559e-05
4.96160268e-05 4.81618643e-05 4.72893667e-05 4.81036978e-05
5.04303579e-05 5.29896840e-05 5.21753529e-05 5.07793569e-05
5.07793569e-05 4.86853628e-05 4.69403677e-05 4.81618643e-05
4.82781973e-05 4.81618643e-05 5.07793569e-05 5.35713490e-05
5.22335194e-05 4.75801992e-05 4.58352041e-05 4.69985342e-05
4.81618643e-05 4.87435293e-05 4.96741933e-05 5.00231923e-05
5.07793569e-05 5.29896840e-05 5.66541737e-05 5.95043323e-05
5.74685047e-05 5.28733510e-05 4.93251943e-05 4.89761953e-05
5.10120229e-05 5.35131825e-05 5.57235096e-05 5.53163441e-05
5.34550160e-05 5.26406850e-05 5.27570180e-05 5.14773549e-05
4.89180288e-05 4.81618643e-05 5.00231923e-05 5.22916860e-05
5.31641835e-05 5.24661855e-05 4.95578603e-05 4.61260367e-05
4.37993766e-05 4.29850455e-05 4.41483756e-05 4.58933707e-05
4.86271963e-05 5.06630239e-05 5.13610219e-05 5.08956899e-05
5.11283559e-05 5.10120229e-05 5.03140249e-05 5.03140249e-05
4.99650258e-05 4.89180288e-05 4.68822012e-05 4.57770376e-05
4.99068593e-05 5.42693470e-05 5.25825185e-05 5.00231923e-05
5.15355214e-05 5.22335194e-05 5.01976919e-05 5.00231923e-05
5.04303579e-05 5.10701894e-05 5.20008534e-05 5.00813588e-05
4.67077017e-05 4.61842032e-05 4.68240347e-05 4.71730337e-05
4.79873647e-05 4.70567007e-05 4.66495352e-05 4.80455313e-05
4.92670278e-05 4.92088613e-05 4.88598623e-05 4.97905263e-05
5.04303579e-05 5.12446889e-05 5.28151845e-05 5.35713490e-05
5.21753529e-05 5.06048574e-05 5.22335194e-05 5.52000111e-05
5.48510121e-05 5.44438466e-05 5.42111805e-05 5.24661855e-05
5.22335194e-05 5.21753529e-05 5.11865224e-05 5.18263539e-05
5.38621815e-05 5.49091786e-05 5.53163441e-05 5.55490101e-05
5.56653431e-05 5.49091786e-05 5.35713490e-05 5.42693470e-05
5.53163441e-05 5.32223500e-05 5.08956899e-05 5.11283559e-05
5.19426869e-05 5.36876820e-05 5.40948475e-05 5.16518544e-05
5.01976919e-05 5.04303579e-05 5.14773549e-05 5.31641835e-05
5.43856800e-05 5.40366810e-05 5.37458485e-05 5.62470081e-05
6.03186633e-05 4.90343618e-05 4.01348869e-05 6.74731432e-05
8.13167708e-05 6.07258288e-05 5.01976919e-05 5.86318347e-05
6.49719835e-05 6.59026476e-05 6.25871569e-05 4.61842032e-05
5.65960072e-05 9.89412211e-05 1.03129209e-04 4.89180288e-05
1.69264523e-05 6.72404772e-05 1.08771360e-04 8.53302595e-05
6.13656604e-05 6.66588121e-05 7.15447984e-05 7.15447984e-05
7.50347885e-05 7.78849471e-05 7.81176132e-05 7.82921127e-05
7.67797836e-05 7.63144516e-05 8.18402693e-05 7.91064437e-05
5.43856800e-05 5.89226673e-05 8.45159285e-05 8.02116073e-05
6.55536486e-05 6.84038072e-05 7.48602890e-05 7.38714585e-05
7.54419540e-05 8.10259383e-05 8.05024398e-05 7.42204575e-05
6.99161363e-05 6.88691392e-05 6.83456407e-05 6.69496446e-05
6.77058092e-05 7.11957993e-05 7.42786240e-05 7.74196151e-05
7.93391097e-05 7.63726181e-05 6.99161363e-05 6.57281481e-05
6.52628161e-05 6.54954821e-05 6.47974840e-05 6.32851550e-05
6.13074939e-05 6.14819934e-05 6.48556505e-05 6.90436387e-05
6.96834703e-05 6.63098131e-05 6.37504870e-05 6.36341540e-05
6.29361559e-05 6.31106555e-05 6.51464831e-05 6.71823106e-05
6.56699816e-05 6.12493274e-05 5.90971668e-05 6.08421619e-05
6.11329944e-05 5.83991687e-05 5.90971668e-05 6.34596545e-05
6.43321520e-05 6.06094958e-05 5.74103382e-05 5.60725086e-05
5.60725086e-05 5.68868397e-05 5.66541737e-05 5.59561756e-05
5.54908436e-05 5.21753529e-05 4.92088613e-05 5.21171864e-05
5.63633411e-05 5.72358387e-05 5.78175037e-05 5.65960072e-05
5.38040150e-05 5.33968495e-05 5.35713490e-05 5.24661855e-05
5.13028554e-05 5.06630239e-05 5.14773549e-05 5.39203480e-05
5.38621815e-05 5.19426869e-05 5.20590199e-05 5.26406850e-05
5.27570180e-05 5.27570180e-05 5.18263539e-05 4.97905263e-05
4.69403677e-05 4.69403677e-05 5.06048574e-05 5.13610219e-05
5.00813588e-05 5.34550160e-05 5.78175037e-05 5.56071766e-05
5.01976919e-05 4.85690298e-05 4.98486928e-05 4.79873647e-05
4.45555411e-05 4.28687125e-05 4.24033805e-05 4.40902091e-05
4.50790396e-05 4.39157096e-05 4.33340445e-05 4.58352041e-05
4.64168692e-05 4.24615470e-05 3.97277214e-05 4.33340445e-05
4.89761953e-05 5.11283559e-05 5.13028554e-05 5.13610219e-05
5.09538564e-05 5.22335194e-05 5.45601796e-05 5.67705067e-05
5.91553333e-05 6.00859973e-05 5.91553333e-05 5.72940052e-05
5.50836781e-05 5.55490101e-05 5.91553333e-05 6.04931628e-05
5.70613392e-05 5.40948475e-05 5.56653431e-05 5.63633411e-05
5.26406850e-05 4.89761953e-05 4.75801992e-05 4.58933707e-05
4.40902091e-05 4.40320426e-05 4.44392081e-05 4.44973746e-05
4.61842032e-05 4.64168692e-05 4.32758780e-05 4.22870475e-05
4.65332022e-05 4.95578603e-05 4.73475332e-05 4.52535391e-05
4.58352041e-05 4.71148672e-05 4.61842032e-05 4.44392081e-05
4.47300406e-05 4.51372061e-05 4.43810416e-05 4.31595450e-05
4.21707145e-05 4.24033805e-05 4.35085440e-05 4.54280386e-05
4.52535391e-05 4.37993766e-05 4.27523795e-05 4.15308829e-05
4.00767204e-05 3.97277214e-05 4.18217155e-05 4.39738761e-05
4.25197135e-05 4.03093864e-05 4.12400504e-05 4.31013785e-05
4.38575431e-05 4.47882071e-05 4.50790396e-05 4.46718741e-05
4.29850455e-05 3.99603874e-05 3.86807243e-05 3.99022209e-05
4.22288810e-05 4.44973746e-05 4.39738761e-05 4.14727164e-05
4.18798820e-05 4.47300406e-05 4.46718741e-05 4.24615470e-05
4.28687125e-05 4.37412100e-05 4.22288810e-05 4.35085440e-05
4.76383657e-05 4.91506948e-05 4.75801992e-05 4.54280386e-05
4.60678702e-05 4.87435293e-05 5.16518544e-05 5.20590199e-05
4.68240347e-05 4.20543815e-05 4.50208731e-05 4.81036978e-05
4.65332022e-05 4.68240347e-05 4.93833608e-05 5.00813588e-05
5.00231923e-05 5.13610219e-05 5.29315175e-05 5.34550160e-05
5.15355214e-05 4.92670278e-05 4.91506948e-05 5.08956899e-05
5.31641835e-05 5.57816761e-05 5.99696643e-05 6.20636584e-05
5.97951648e-05 5.39203480e-05 5.19426869e-05 5.70613392e-05
5.87481678e-05 5.50255116e-05 5.34550160e-05 5.46765126e-05
5.41530140e-05 5.21753529e-05 5.09538564e-05 5.18845204e-05
5.19426869e-05 4.85108633e-05 4.53117056e-05 4.43228751e-05
3.95532219e-05 4.25778800e-05 5.50255116e-05 5.66541737e-05
5.08375234e-05 4.80455313e-05 4.82781973e-05 4.69985342e-05
4.51953726e-05 4.31595450e-05 4.19962150e-05 4.41483756e-05
4.61842032e-05 4.46137076e-05 4.46137076e-05 4.93251943e-05
5.13028554e-05 4.56025381e-05 4.30432120e-05 4.61842032e-05
4.57188711e-05 4.19380485e-05 4.19380485e-05 4.37993766e-05
4.06583854e-05 3.56560662e-05 3.73428947e-05 4.32758780e-05
4.62423697e-05 4.51953726e-05 4.40320426e-05 4.41483756e-05
4.12400504e-05 3.57142327e-05 3.42019036e-05 3.72265617e-05
3.94950554e-05 3.97858879e-05 4.06002189e-05 4.32177115e-05
4.46718741e-05 4.37412100e-05 4.58933707e-05 4.83363638e-05
4.60678702e-05 4.36830435e-05 4.46718741e-05 4.60678702e-05
4.49627066e-05 4.42065421e-05 4.49045401e-05 4.37993766e-05
4.04257194e-05 3.87970573e-05 4.20543815e-05 4.57188711e-05
4.66495352e-05 4.75801992e-05 4.87435293e-05 4.94996938e-05
4.86853628e-05 4.67077017e-05 4.39157096e-05 4.32758780e-05
4.46718741e-05 4.53698721e-05 4.49627066e-05 4.41483756e-05
4.32758780e-05 4.38575431e-05 4.47300406e-05 4.56025381e-05
4.77546987e-05 5.08375234e-05 5.25825185e-05 5.25243520e-05
5.31060170e-05 5.31060170e-05 5.36876820e-05 5.52581776e-05
5.59561756e-05 5.57235096e-05 5.36295155e-05 5.18845204e-05
5.31641835e-05 5.35131825e-05 5.01976919e-05 4.82781973e-05
4.75801992e-05 4.68822012e-05 4.50790396e-05 4.30432120e-05
4.35667105e-05 4.33922110e-05 4.07165519e-05 3.97277214e-05
4.00185539e-05 4.01348869e-05 4.00767204e-05 3.84480583e-05
3.57723992e-05 3.31549066e-05 3.21079095e-05 3.50162346e-05
3.99022209e-05 4.18798820e-05 4.03675529e-05 3.82153923e-05
3.90878898e-05 4.49627066e-05 4.98486928e-05 5.20590199e-05
5.52581776e-05 5.79338367e-05 5.68868397e-05 5.52581776e-05
5.77593372e-05 6.09584949e-05 5.88645008e-05 5.33386830e-05
5.19426869e-05 5.42693470e-05 5.62470081e-05 5.77011707e-05
5.95043323e-05 5.84573352e-05 5.43275135e-05 5.26406850e-05
5.43275135e-05 5.77011707e-05 6.02604968e-05 6.09584949e-05
6.00859973e-05 5.70031727e-05 5.23498525e-05 5.23498525e-05
5.65960072e-05 5.49091786e-05 4.90925283e-05 4.68822012e-05
5.00231923e-05 5.39203480e-05 5.24661855e-05 5.04303579e-05
5.14773549e-05 4.90343618e-05 4.53698721e-05 4.69985342e-05
5.20008534e-05 5.46183461e-05 5.37458485e-05 5.14773549e-05
4.98486928e-05 5.33968495e-05 5.99696643e-05 6.47974840e-05
6.76476427e-05 6.72404772e-05 6.29361559e-05 6.04349963e-05
6.35178210e-05 6.56699816e-05 6.15983264e-05 5.60143421e-05
5.43275135e-05 5.38040150e-05 5.02558584e-05 4.83363638e-05
4.89180288e-05 4.89761953e-05 4.79291982e-05 4.60678702e-05
4.36248770e-05 4.04257194e-05 3.85643913e-05 4.11818839e-05
4.56607046e-05 4.53698721e-05 4.14145499e-05 3.84480583e-05
3.80408928e-05 3.99603874e-05 4.15890494e-05 4.07747184e-05
4.04257194e-05 3.90297233e-05 3.96113884e-05 4.54280386e-05
4.88016958e-05 4.51372061e-05 4.07747184e-05 4.07747184e-05
4.18798820e-05 4.22870475e-05 4.35667105e-05 4.43810416e-05
4.22288810e-05 3.94950554e-05 4.07747184e-05 4.43810416e-05
4.42647086e-05 4.15890494e-05 4.23452140e-05 4.48463736e-05
4.60678702e-05 4.60678702e-05 4.58933707e-05 4.58352041e-05
4.43228751e-05 4.30432120e-05 4.41483756e-05 4.43810416e-05
4.21125480e-05 4.21125480e-05 4.43228751e-05 4.21125480e-05
3.89133903e-05 3.85062248e-05 3.96695549e-05 4.09492179e-05
3.98440544e-05 3.59468987e-05 3.87970573e-05 4.68822012e-05
4.87435293e-05 4.51953726e-05 4.49627066e-05 4.51953726e-05
4.34503775e-05 4.37412100e-05 4.31595450e-05 4.08910514e-05
3.97858879e-05 3.96113884e-05 3.50744011e-05 3.05955804e-05
3.94950554e-05 4.39157096e-05 3.86225578e-05 3.89715568e-05
4.36248770e-05 4.37993766e-05 4.14145499e-05 4.26360465e-05
4.51372061e-05 4.45555411e-05 4.28687125e-05 4.29268790e-05
4.36248770e-05 4.41483756e-05 4.64168692e-05 4.86853628e-05
4.81036978e-05 4.64750357e-05 4.67658682e-05 4.89180288e-05
4.90343618e-05 4.62423697e-05 4.40902091e-05 4.57188711e-05
4.55443716e-05 4.37993766e-05 4.81036978e-05 5.34550160e-05
5.24080190e-05 4.79873647e-05 4.61260367e-05 4.74056997e-05
4.96741933e-05 5.04303579e-05 4.97905263e-05 4.93251943e-05
4.87435293e-05 4.82200308e-05 4.93833608e-05 5.05466909e-05
5.02558584e-05 4.93251943e-05 4.71148672e-05 4.45555411e-05
4.21707145e-05 3.87388908e-05 4.00185539e-05 4.59515372e-05
4.64750357e-05 3.90297233e-05 3.36202386e-05 3.78082268e-05
4.44392081e-05 4.67077017e-05 4.64168692e-05 4.33922110e-05
3.97277214e-05 4.19962150e-05 4.47300406e-05 4.30432120e-05
4.03675529e-05 3.94950554e-05 3.99603874e-05 4.19962150e-05
4.28687125e-05 3.92623893e-05 3.70520622e-05 3.89715568e-05
3.97277214e-05 4.18217155e-05 4.45555411e-05 4.14145499e-05
3.83317253e-05 3.97277214e-05 4.01348869e-05 4.07747184e-05
4.25778800e-05 4.40320426e-05 4.31013785e-05 4.22870475e-05
4.60097037e-05 5.11283559e-05 5.07793569e-05 4.88016958e-05
5.24661855e-05 5.83410022e-05 5.75848377e-05 5.17100209e-05
5.22335194e-05 5.61888416e-05 5.54326771e-05 5.56071766e-05
5.79920032e-05 5.88645008e-05 5.81083362e-05 5.30478505e-05
4.74638662e-05 5.74103382e-05 6.54373156e-05 5.96206653e-05
5.25243520e-05 5.38621815e-05 5.79920032e-05 5.82828357e-05
5.63051746e-05 5.63051746e-05 5.81665027e-05 5.64796741e-05
5.04303579e-05 4.99068593e-05 5.52000111e-05 5.72940052e-05
5.41530140e-05 5.27570180e-05 5.85155017e-05 6.21218249e-05
5.81665027e-05 5.49673451e-05 5.72940052e-05 5.95043323e-05
5.96206653e-05 6.09003284e-05 6.02023303e-05 5.88645008e-05
6.21218249e-05 6.49138170e-05 6.41576525e-05 6.36341540e-05
6.10166614e-05 5.92716663e-05 6.37504870e-05 6.56118151e-05
5.89808338e-05 5.26406850e-05 5.29315175e-05 5.43275135e-05
5.27570180e-05 4.95578603e-05 4.75220327e-05 4.62423697e-05
4.67077017e-05 4.91506948e-05 4.97905263e-05 4.84526968e-05
4.74638662e-05 4.76383657e-05 4.87435293e-05 5.03721914e-05
5.32805165e-05 5.47346791e-05 5.04885244e-05 4.55443716e-05
4.53117056e-05 4.65332022e-05 4.57188711e-05 4.74056997e-05
5.08375234e-05 4.85108633e-05 4.38575431e-05 4.53117056e-05
4.69403677e-05 4.46718741e-05 4.37993766e-05 4.39738761e-05
4.33340445e-05 4.27523795e-05 3.99022209e-05 3.66448967e-05]
which is the same as
print(raw[['EEG 054'], 1000:2000][0])
[[4.68822012e-05 4.50790396e-05 4.54280386e-05 4.77546987e-05
4.80455313e-05 4.66495352e-05 4.54862051e-05 4.72893667e-05
5.11865224e-05 5.16518544e-05 5.00813588e-05 4.96160268e-05
4.85690298e-05 4.60678702e-05 4.49627066e-05 4.51372061e-05
4.68822012e-05 4.94415273e-05 5.03721914e-05 4.92670278e-05
4.97323598e-05 5.13028554e-05 5.20008534e-05 5.31641835e-05
5.32223500e-05 5.05466909e-05 4.89180288e-05 4.96741933e-05
5.06048574e-05 5.10701894e-05 5.26406850e-05 5.48510121e-05
5.49673451e-05 5.42111805e-05 5.43856800e-05 5.38621815e-05
5.32223500e-05 5.27570180e-05 5.22916860e-05 5.13028554e-05
5.26406850e-05 5.51418446e-05 5.54326771e-05 5.47928456e-05
5.53163441e-05 5.58398426e-05 5.60725086e-05 5.57235096e-05
5.57816761e-05 5.77011707e-05 6.03186633e-05 6.00278308e-05
5.83410022e-05 5.99696643e-05 6.15401599e-05 5.81665027e-05
5.36876820e-05 5.07793569e-05 4.90343618e-05 4.92088613e-05
5.00231923e-05 5.08956899e-05 5.31060170e-05 5.25825185e-05
4.76965322e-05 4.54862051e-05 4.84526968e-05 5.09538564e-05
4.89180288e-05 4.55443716e-05 4.53117056e-05 4.49627066e-05
4.28105460e-05 4.10073844e-05 4.18217155e-05 4.39157096e-05
4.57770376e-05 4.61260367e-05 4.47882071e-05 4.39738761e-05
4.60678702e-05 5.01976919e-05 5.26406850e-05 5.13028554e-05
4.71730337e-05 4.30432120e-05 4.13563834e-05 4.24615470e-05
4.54862051e-05 4.75801992e-05 4.64168692e-05 4.37993766e-05
4.19962150e-05 4.17635490e-05 4.12400504e-05 4.07165519e-05
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3.80990593e-05 3.73428947e-05 3.97858879e-05 4.00767204e-05
3.93205558e-05 4.04838859e-05 4.17635490e-05 4.12400504e-05
4.04257194e-05 3.94950554e-05 3.94950554e-05 4.06583854e-05
3.88552238e-05 3.57142327e-05 3.51325676e-05 3.57142327e-05
3.68193962e-05 3.69357292e-05 3.50162346e-05 3.51325676e-05
3.67030632e-05 3.57723992e-05 3.48417351e-05 3.72265617e-05
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6.31106555e-05 6.21799914e-05 6.19473254e-05 6.04349963e-05
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6.03186633e-05 4.90343618e-05 4.01348869e-05 6.74731432e-05
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5.65960072e-05 9.89412211e-05 1.03129209e-04 4.89180288e-05
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4.45555411e-05 4.28687125e-05 4.24033805e-05 4.40902091e-05
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3.95532219e-05 4.25778800e-05 5.50255116e-05 5.66541737e-05
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3.57723992e-05 3.31549066e-05 3.21079095e-05 3.50162346e-05
3.99022209e-05 4.18798820e-05 4.03675529e-05 3.82153923e-05
3.90878898e-05 4.49627066e-05 4.98486928e-05 5.20590199e-05
5.52581776e-05 5.79338367e-05 5.68868397e-05 5.52581776e-05
5.77593372e-05 6.09584949e-05 5.88645008e-05 5.33386830e-05
5.19426869e-05 5.42693470e-05 5.62470081e-05 5.77011707e-05
5.95043323e-05 5.84573352e-05 5.43275135e-05 5.26406850e-05
5.43275135e-05 5.77011707e-05 6.02604968e-05 6.09584949e-05
6.00859973e-05 5.70031727e-05 5.23498525e-05 5.23498525e-05
5.65960072e-05 5.49091786e-05 4.90925283e-05 4.68822012e-05
5.00231923e-05 5.39203480e-05 5.24661855e-05 5.04303579e-05
5.14773549e-05 4.90343618e-05 4.53698721e-05 4.69985342e-05
5.20008534e-05 5.46183461e-05 5.37458485e-05 5.14773549e-05
4.98486928e-05 5.33968495e-05 5.99696643e-05 6.47974840e-05
6.76476427e-05 6.72404772e-05 6.29361559e-05 6.04349963e-05
6.35178210e-05 6.56699816e-05 6.15983264e-05 5.60143421e-05
5.43275135e-05 5.38040150e-05 5.02558584e-05 4.83363638e-05
4.89180288e-05 4.89761953e-05 4.79291982e-05 4.60678702e-05
4.36248770e-05 4.04257194e-05 3.85643913e-05 4.11818839e-05
4.56607046e-05 4.53698721e-05 4.14145499e-05 3.84480583e-05
3.80408928e-05 3.99603874e-05 4.15890494e-05 4.07747184e-05
4.04257194e-05 3.90297233e-05 3.96113884e-05 4.54280386e-05
4.88016958e-05 4.51372061e-05 4.07747184e-05 4.07747184e-05
4.18798820e-05 4.22870475e-05 4.35667105e-05 4.43810416e-05
4.22288810e-05 3.94950554e-05 4.07747184e-05 4.43810416e-05
4.42647086e-05 4.15890494e-05 4.23452140e-05 4.48463736e-05
4.60678702e-05 4.60678702e-05 4.58933707e-05 4.58352041e-05
4.43228751e-05 4.30432120e-05 4.41483756e-05 4.43810416e-05
4.21125480e-05 4.21125480e-05 4.43228751e-05 4.21125480e-05
3.89133903e-05 3.85062248e-05 3.96695549e-05 4.09492179e-05
3.98440544e-05 3.59468987e-05 3.87970573e-05 4.68822012e-05
4.87435293e-05 4.51953726e-05 4.49627066e-05 4.51953726e-05
4.34503775e-05 4.37412100e-05 4.31595450e-05 4.08910514e-05
3.97858879e-05 3.96113884e-05 3.50744011e-05 3.05955804e-05
3.94950554e-05 4.39157096e-05 3.86225578e-05 3.89715568e-05
4.36248770e-05 4.37993766e-05 4.14145499e-05 4.26360465e-05
4.51372061e-05 4.45555411e-05 4.28687125e-05 4.29268790e-05
4.36248770e-05 4.41483756e-05 4.64168692e-05 4.86853628e-05
4.81036978e-05 4.64750357e-05 4.67658682e-05 4.89180288e-05
4.90343618e-05 4.62423697e-05 4.40902091e-05 4.57188711e-05
4.55443716e-05 4.37993766e-05 4.81036978e-05 5.34550160e-05
5.24080190e-05 4.79873647e-05 4.61260367e-05 4.74056997e-05
4.96741933e-05 5.04303579e-05 4.97905263e-05 4.93251943e-05
4.87435293e-05 4.82200308e-05 4.93833608e-05 5.05466909e-05
5.02558584e-05 4.93251943e-05 4.71148672e-05 4.45555411e-05
4.21707145e-05 3.87388908e-05 4.00185539e-05 4.59515372e-05
4.64750357e-05 3.90297233e-05 3.36202386e-05 3.78082268e-05
4.44392081e-05 4.67077017e-05 4.64168692e-05 4.33922110e-05
3.97277214e-05 4.19962150e-05 4.47300406e-05 4.30432120e-05
4.03675529e-05 3.94950554e-05 3.99603874e-05 4.19962150e-05
4.28687125e-05 3.92623893e-05 3.70520622e-05 3.89715568e-05
3.97277214e-05 4.18217155e-05 4.45555411e-05 4.14145499e-05
3.83317253e-05 3.97277214e-05 4.01348869e-05 4.07747184e-05
4.25778800e-05 4.40320426e-05 4.31013785e-05 4.22870475e-05
4.60097037e-05 5.11283559e-05 5.07793569e-05 4.88016958e-05
5.24661855e-05 5.83410022e-05 5.75848377e-05 5.17100209e-05
5.22335194e-05 5.61888416e-05 5.54326771e-05 5.56071766e-05
5.79920032e-05 5.88645008e-05 5.81083362e-05 5.30478505e-05
4.74638662e-05 5.74103382e-05 6.54373156e-05 5.96206653e-05
5.25243520e-05 5.38621815e-05 5.79920032e-05 5.82828357e-05
5.63051746e-05 5.63051746e-05 5.81665027e-05 5.64796741e-05
5.04303579e-05 4.99068593e-05 5.52000111e-05 5.72940052e-05
5.41530140e-05 5.27570180e-05 5.85155017e-05 6.21218249e-05
5.81665027e-05 5.49673451e-05 5.72940052e-05 5.95043323e-05
5.96206653e-05 6.09003284e-05 6.02023303e-05 5.88645008e-05
6.21218249e-05 6.49138170e-05 6.41576525e-05 6.36341540e-05
6.10166614e-05 5.92716663e-05 6.37504870e-05 6.56118151e-05
5.89808338e-05 5.26406850e-05 5.29315175e-05 5.43275135e-05
5.27570180e-05 4.95578603e-05 4.75220327e-05 4.62423697e-05
4.67077017e-05 4.91506948e-05 4.97905263e-05 4.84526968e-05
4.74638662e-05 4.76383657e-05 4.87435293e-05 5.03721914e-05
5.32805165e-05 5.47346791e-05 5.04885244e-05 4.55443716e-05
4.53117056e-05 4.65332022e-05 4.57188711e-05 4.74056997e-05
5.08375234e-05 4.85108633e-05 4.38575431e-05 4.53117056e-05
4.69403677e-05 4.46718741e-05 4.37993766e-05 4.39738761e-05
4.33340445e-05 4.27523795e-05 3.99022209e-05 3.66448967e-05]]
# check if the the abpve example truly accesses the same data
print(raw[['EEG 054'], 1000:2000][0] == raw[['EEG 054', 'EEG 055'], 1000:2000][0][0])
[[ True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True]]
the same applies to our time samples
# let's take a look at how to access the corresponding time-samples
print('number of time samples')
print(' ')
print(raw[['EEG 054', 'EEG 055'], 1000:2000][1].shape)
print(' ')
print('-------------------------------------------------------')
print('time for sample in array')
print(' ')
print(raw[['EEG 054', 'EEG 055'], 1000:2000][1]) # access corresponding time points
number of time samples
(1000,)
-------------------------------------------------------
time for sample in array
[1.66496011 1.66662507 1.66829003 1.66995499 1.67161995 1.67328491
1.67494987 1.67661483 1.67827979 1.67994475 1.68160971 1.68327467
1.68493963 1.68660459 1.68826955 1.68993451 1.69159947 1.69326443
1.69492939 1.69659435 1.69825931 1.69992427 1.70158923 1.70325419
1.70491915 1.70658411 1.70824907 1.70991403 1.71157899 1.71324395
1.71490891 1.71657387 1.71823883 1.71990379 1.72156875 1.72323371
1.72489867 1.72656363 1.72822859 1.72989355 1.73155851 1.73322347
1.73488843 1.73655339 1.73821835 1.73988331 1.74154827 1.74321323
1.74487819 1.74654316 1.74820812 1.74987308 1.75153804 1.753203
1.75486796 1.75653292 1.75819788 1.75986284 1.7615278 1.76319276
1.76485772 1.76652268 1.76818764 1.7698526 1.77151756 1.77318252
1.77484748 1.77651244 1.7781774 1.77984236 1.78150732 1.78317228
1.78483724 1.7865022 1.78816716 1.78983212 1.79149708 1.79316204
1.794827 1.79649196 1.79815692 1.79982188 1.80148684 1.8031518
1.80481676 1.80648172 1.80814668 1.80981164 1.8114766 1.81314156
1.81480652 1.81647148 1.81813644 1.8198014 1.82146636 1.82313132
1.82479628 1.82646124 1.8281262 1.82979116 1.83145612 1.83312108
1.83478604 1.836451 1.83811596 1.83978092 1.84144588 1.84311084
1.8447758 1.84644076 1.84810572 1.84977068 1.85143564 1.8531006
1.85476556 1.85643052 1.85809548 1.85976044 1.8614254 1.86309036
1.86475532 1.86642028 1.86808524 1.8697502 1.87141516 1.87308012
1.87474508 1.87641004 1.878075 1.87973996 1.88140492 1.88306988
1.88473484 1.8863998 1.88806476 1.88972972 1.89139468 1.89305964
1.8947246 1.89638956 1.89805453 1.89971949 1.90138445 1.90304941
1.90471437 1.90637933 1.90804429 1.90970925 1.91137421 1.91303917
1.91470413 1.91636909 1.91803405 1.91969901 1.92136397 1.92302893
1.92469389 1.92635885 1.92802381 1.92968877 1.93135373 1.93301869
1.93468365 1.93634861 1.93801357 1.93967853 1.94134349 1.94300845
1.94467341 1.94633837 1.94800333 1.94966829 1.95133325 1.95299821
1.95466317 1.95632813 1.95799309 1.95965805 1.96132301 1.96298797
1.96465293 1.96631789 1.96798285 1.96964781 1.97131277 1.97297773
1.97464269 1.97630765 1.97797261 1.97963757 1.98130253 1.98296749
1.98463245 1.98629741 1.98796237 1.98962733 1.99129229 1.99295725
1.99462221 1.99628717 1.99795213 1.99961709 2.00128205 2.00294701
2.00461197 2.00627693 2.00794189 2.00960685 2.01127181 2.01293677
2.01460173 2.01626669 2.01793165 2.01959661 2.02126157 2.02292653
2.02459149 2.02625645 2.02792141 2.02958637 2.03125133 2.03291629
2.03458125 2.03624621 2.03791117 2.03957613 2.04124109 2.04290605
2.04457101 2.04623597 2.04790093 2.04956589 2.05123086 2.05289582
2.05456078 2.05622574 2.0578907 2.05955566 2.06122062 2.06288558
2.06455054 2.0662155 2.06788046 2.06954542 2.07121038 2.07287534
2.0745403 2.07620526 2.07787022 2.07953518 2.08120014 2.0828651
2.08453006 2.08619502 2.08785998 2.08952494 2.0911899 2.09285486
2.09451982 2.09618478 2.09784974 2.0995147 2.10117966 2.10284462
2.10450958 2.10617454 2.1078395 2.10950446 2.11116942 2.11283438
2.11449934 2.1161643 2.11782926 2.11949422 2.12115918 2.12282414
2.1244891 2.12615406 2.12781902 2.12948398 2.13114894 2.1328139
2.13447886 2.13614382 2.13780878 2.13947374 2.1411387 2.14280366
2.14446862 2.14613358 2.14779854 2.1494635 2.15112846 2.15279342
2.15445838 2.15612334 2.1577883 2.15945326 2.16111822 2.16278318
2.16444814 2.1661131 2.16777806 2.16944302 2.17110798 2.17277294
2.1744379 2.17610286 2.17776782 2.17943278 2.18109774 2.1827627
2.18442766 2.18609262 2.18775758 2.18942254 2.1910875 2.19275246
2.19441742 2.19608238 2.19774734 2.1994123 2.20107726 2.20274223
2.20440719 2.20607215 2.20773711 2.20940207 2.21106703 2.21273199
2.21439695 2.21606191 2.21772687 2.21939183 2.22105679 2.22272175
2.22438671 2.22605167 2.22771663 2.22938159 2.23104655 2.23271151
2.23437647 2.23604143 2.23770639 2.23937135 2.24103631 2.24270127
2.24436623 2.24603119 2.24769615 2.24936111 2.25102607 2.25269103
2.25435599 2.25602095 2.25768591 2.25935087 2.26101583 2.26268079
2.26434575 2.26601071 2.26767567 2.26934063 2.27100559 2.27267055
2.27433551 2.27600047 2.27766543 2.27933039 2.28099535 2.28266031
2.28432527 2.28599023 2.28765519 2.28932015 2.29098511 2.29265007
2.29431503 2.29597999 2.29764495 2.29930991 2.30097487 2.30263983
2.30430479 2.30596975 2.30763471 2.30929967 2.31096463 2.31262959
2.31429455 2.31595951 2.31762447 2.31928943 2.32095439 2.32261935
2.32428431 2.32594927 2.32761423 2.32927919 2.33094415 2.33260911
2.33427407 2.33593903 2.33760399 2.33926895 2.34093391 2.34259887
2.34426383 2.34592879 2.34759375 2.34925871 2.35092367 2.35258863
2.3542536 2.35591856 2.35758352 2.35924848 2.36091344 2.3625784
2.36424336 2.36590832 2.36757328 2.36923824 2.3709032 2.37256816
2.37423312 2.37589808 2.37756304 2.379228 2.38089296 2.38255792
2.38422288 2.38588784 2.3875528 2.38921776 2.39088272 2.39254768
2.39421264 2.3958776 2.39754256 2.39920752 2.40087248 2.40253744
2.4042024 2.40586736 2.40753232 2.40919728 2.41086224 2.4125272
2.41419216 2.41585712 2.41752208 2.41918704 2.420852 2.42251696
2.42418192 2.42584688 2.42751184 2.4291768 2.43084176 2.43250672
2.43417168 2.43583664 2.4375016 2.43916656 2.44083152 2.44249648
2.44416144 2.4458264 2.44749136 2.44915632 2.45082128 2.45248624
2.4541512 2.45581616 2.45748112 2.45914608 2.46081104 2.462476
2.46414096 2.46580592 2.46747088 2.46913584 2.4708008 2.47246576
2.47413072 2.47579568 2.47746064 2.4791256 2.48079056 2.48245552
2.48412048 2.48578544 2.4874504 2.48911536 2.49078032 2.49244528
2.49411024 2.4957752 2.49744016 2.49910512 2.50077008 2.50243504
2.5041 2.50576497 2.50742993 2.50909489 2.51075985 2.51242481
2.51408977 2.51575473 2.51741969 2.51908465 2.52074961 2.52241457
2.52407953 2.52574449 2.52740945 2.52907441 2.53073937 2.53240433
2.53406929 2.53573425 2.53739921 2.53906417 2.54072913 2.54239409
2.54405905 2.54572401 2.54738897 2.54905393 2.55071889 2.55238385
2.55404881 2.55571377 2.55737873 2.55904369 2.56070865 2.56237361
2.56403857 2.56570353 2.56736849 2.56903345 2.57069841 2.57236337
2.57402833 2.57569329 2.57735825 2.57902321 2.58068817 2.58235313
2.58401809 2.58568305 2.58734801 2.58901297 2.59067793 2.59234289
2.59400785 2.59567281 2.59733777 2.59900273 2.60066769 2.60233265
2.60399761 2.60566257 2.60732753 2.60899249 2.61065745 2.61232241
2.61398737 2.61565233 2.61731729 2.61898225 2.62064721 2.62231217
2.62397713 2.62564209 2.62730705 2.62897201 2.63063697 2.63230193
2.63396689 2.63563185 2.63729681 2.63896177 2.64062673 2.64229169
2.64395665 2.64562161 2.64728657 2.64895153 2.65061649 2.65228145
2.65394641 2.65561137 2.65727634 2.6589413 2.66060626 2.66227122
2.66393618 2.66560114 2.6672661 2.66893106 2.67059602 2.67226098
2.67392594 2.6755909 2.67725586 2.67892082 2.68058578 2.68225074
2.6839157 2.68558066 2.68724562 2.68891058 2.69057554 2.6922405
2.69390546 2.69557042 2.69723538 2.69890034 2.7005653 2.70223026
2.70389522 2.70556018 2.70722514 2.7088901 2.71055506 2.71222002
2.71388498 2.71554994 2.7172149 2.71887986 2.72054482 2.72220978
2.72387474 2.7255397 2.72720466 2.72886962 2.73053458 2.73219954
2.7338645 2.73552946 2.73719442 2.73885938 2.74052434 2.7421893
2.74385426 2.74551922 2.74718418 2.74884914 2.7505141 2.75217906
2.75384402 2.75550898 2.75717394 2.7588389 2.76050386 2.76216882
2.76383378 2.76549874 2.7671637 2.76882866 2.77049362 2.77215858
2.77382354 2.7754885 2.77715346 2.77881842 2.78048338 2.78214834
2.7838133 2.78547826 2.78714322 2.78880818 2.79047314 2.7921381
2.79380306 2.79546802 2.79713298 2.79879794 2.8004629 2.80212786
2.80379282 2.80545778 2.80712274 2.8087877 2.81045267 2.81211763
2.81378259 2.81544755 2.81711251 2.81877747 2.82044243 2.82210739
2.82377235 2.82543731 2.82710227 2.82876723 2.83043219 2.83209715
2.83376211 2.83542707 2.83709203 2.83875699 2.84042195 2.84208691
2.84375187 2.84541683 2.84708179 2.84874675 2.85041171 2.85207667
2.85374163 2.85540659 2.85707155 2.85873651 2.86040147 2.86206643
2.86373139 2.86539635 2.86706131 2.86872627 2.87039123 2.87205619
2.87372115 2.87538611 2.87705107 2.87871603 2.88038099 2.88204595
2.88371091 2.88537587 2.88704083 2.88870579 2.89037075 2.89203571
2.89370067 2.89536563 2.89703059 2.89869555 2.90036051 2.90202547
2.90369043 2.90535539 2.90702035 2.90868531 2.91035027 2.91201523
2.91368019 2.91534515 2.91701011 2.91867507 2.92034003 2.92200499
2.92366995 2.92533491 2.92699987 2.92866483 2.93032979 2.93199475
2.93365971 2.93532467 2.93698963 2.93865459 2.94031955 2.94198451
2.94364947 2.94531443 2.94697939 2.94864435 2.95030931 2.95197427
2.95363923 2.95530419 2.95696915 2.95863411 2.96029907 2.96196404
2.963629 2.96529396 2.96695892 2.96862388 2.97028884 2.9719538
2.97361876 2.97528372 2.97694868 2.97861364 2.9802786 2.98194356
2.98360852 2.98527348 2.98693844 2.9886034 2.99026836 2.99193332
2.99359828 2.99526324 2.9969282 2.99859316 3.00025812 3.00192308
3.00358804 3.005253 3.00691796 3.00858292 3.01024788 3.01191284
3.0135778 3.01524276 3.01690772 3.01857268 3.02023764 3.0219026
3.02356756 3.02523252 3.02689748 3.02856244 3.0302274 3.03189236
3.03355732 3.03522228 3.03688724 3.0385522 3.04021716 3.04188212
3.04354708 3.04521204 3.046877 3.04854196 3.05020692 3.05187188
3.05353684 3.0552018 3.05686676 3.05853172 3.06019668 3.06186164
3.0635266 3.06519156 3.06685652 3.06852148 3.07018644 3.0718514
3.07351636 3.07518132 3.07684628 3.07851124 3.0801762 3.08184116
3.08350612 3.08517108 3.08683604 3.088501 3.09016596 3.09183092
3.09349588 3.09516084 3.0968258 3.09849076 3.10015572 3.10182068
3.10348564 3.1051506 3.10681556 3.10848052 3.11014548 3.11181044
3.11347541 3.11514037 3.11680533 3.11847029 3.12013525 3.12180021
3.12346517 3.12513013 3.12679509 3.12846005 3.13012501 3.13178997
3.13345493 3.13511989 3.13678485 3.13844981 3.14011477 3.14177973
3.14344469 3.14510965 3.14677461 3.14843957 3.15010453 3.15176949
3.15343445 3.15509941 3.15676437 3.15842933 3.16009429 3.16175925
3.16342421 3.16508917 3.16675413 3.16841909 3.17008405 3.17174901
3.17341397 3.17507893 3.17674389 3.17840885 3.18007381 3.18173877
3.18340373 3.18506869 3.18673365 3.18839861 3.19006357 3.19172853
3.19339349 3.19505845 3.19672341 3.19838837 3.20005333 3.20171829
3.20338325 3.20504821 3.20671317 3.20837813 3.21004309 3.21170805
3.21337301 3.21503797 3.21670293 3.21836789 3.22003285 3.22169781
3.22336277 3.22502773 3.22669269 3.22835765 3.23002261 3.23168757
3.23335253 3.23501749 3.23668245 3.23834741 3.24001237 3.24167733
3.24334229 3.24500725 3.24667221 3.24833717 3.25000213 3.25166709
3.25333205 3.25499701 3.25666197 3.25832693 3.25999189 3.26165685
3.26332181 3.26498678 3.26665174 3.2683167 3.26998166 3.27164662
3.27331158 3.27497654 3.2766415 3.27830646 3.27997142 3.28163638
3.28330134 3.2849663 3.28663126 3.28829622 3.28996118 3.29162614
3.2932911 3.29495606 3.29662102 3.29828598 3.29995094 3.3016159
3.30328086 3.30494582 3.30661078 3.30827574 3.3099407 3.31160566
3.31327062 3.31493558 3.31660054 3.3182655 3.31993046 3.32159542
3.32326038 3.32492534 3.3265903 3.32825526]
We could instead further use the build_in get_data() function to access data, which returns our n_chan x n_samples matrix and if we set return_times=True the corresponding time-points.
data, times = raw.get_data(return_times=True)
print(data.shape)
(376, 166800)
data # channels x samples
array([[ 9.64355481e-12, 0.00000000e+00, 0.00000000e+00, ...,
-1.92871096e-12, 2.89306644e-12, 3.85742192e-12],
[-4.82177740e-12, -2.89306644e-12, -9.64355481e-13, ...,
-9.64355481e-13, -9.64355481e-13, -1.92871096e-12],
[ 1.01074222e-13, 6.31713890e-14, 7.58056668e-14, ...,
-4.80102556e-13, -6.06445334e-13, -5.93811056e-13],
...,
[ 3.88542173e-05, 4.07510373e-05, 4.09957883e-05, ...,
6.72453304e-05, 6.68782039e-05, 6.91421504e-05],
[ 6.58391126e-05, 6.80025648e-05, 6.81779798e-05, ...,
8.51932390e-05, 8.58948991e-05, 8.89938982e-05],
[ 2.85661012e-04, 2.83699953e-04, 2.80431520e-04, ...,
2.64089357e-04, 2.62781984e-04, 2.57552492e-04]])
times # 1 x samples
array([0.00000000e+00, 1.66496011e-03, 3.32992022e-03, ...,
2.77710351e+02, 2.77712016e+02, 2.77713681e+02])
Save/export Data¶
We can export our data in a number of ways for further analysis, that we’ll explore in the following paragraphs.
BIDS data format¶
It’s advisable to adapt common standards for data organisation, so as to make collaboration, data and code sharing and automated analysis easier. For this we’ll use the BIDS data structure, which specifices which file formats to use, file naming conventions and the structure of your data directories.
More info on the BIDS specification can be found here.
Specific tutorials for working with MNE-BIDS can be found here, including tutorials that should cover all of the possible use-cases.
One of the most important things is to understand the BIDSPath object
The most important components are the root directory and our identifiers, i.e. the subject number, task name, session and run number. So we’ll declare those in the next step.
homedir = os.path.expanduser("~") # find home diretory
print(homedir)
root = homedir + (os.sep) + "msc_05_example_bids_dataset" # string denoting where your directory is supposed to be generated
subject = 'test' # convention is a 3 numer identifier i.e 001
task = 'audiovisual'
session = '001'
run = '001'
/home/michael
Let’s set our path
bids_path = BIDSPath(root=root, # set BIDSPath
subject=subject,
task=task,
session=session,
run=run)
bids_path
BIDSPath(
root: /home/michael/msc_05_example_bids_dataset
datatype: None
basename: sub-test_ses-001_task-audiovisual_run-001)
and use the write_raw_bids() function to save raw data to a BIDS-compliant folder structure.
write_raw_bids(raw,
bids_path=bids_path)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/tmp/ipykernel_29088/2930231009.py in <module>
1 write_raw_bids(raw,
----> 2 bids_path=bids_path)
<decorator-gen-585> in write_raw_bids(raw, bids_path, events_data, event_id, anonymize, format, symlink, empty_room, allow_preload, montage, acpc_aligned, overwrite, verbose)
~/anaconda3/envs/neuro_ai/lib/python3.7/site-packages/mne_bids/write.py in write_raw_bids(raw, bids_path, events_data, event_id, anonymize, format, symlink, empty_room, allow_preload, montage, acpc_aligned, overwrite, verbose)
1377
1378 if raw.preload is not False and not allow_preload:
-> 1379 raise ValueError('The data is already loaded from disk and may be '
1380 'altered. See warning for "allow_preload".')
1381
ValueError: The data is already loaded from disk and may be altered. See warning for "allow_preload".
which will result in an error as we’ve loaded our data into memory. MNE-BIDS write_raw_bids() provides a explicit warning for this case:
“BIDS was originally designed for unprocessed or minimally processed data. For this reason, by default, we prevent writing of preloaded data that may have been modified. Only use this option when absolutely necessary: for example, manually converting from file formats not supported by MNE or writing preprocessed derivatives. Be aware that these use cases are not fully supported.”
So it’s advisable to convert your data to the BIDS format beforehand. For this tutorial we’ll just import our data again, without loading it into memory.
raw = mne.io.read_raw_fif(sample_data_raw_file, preload=False)
Opening raw data file /home/michael/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
Read a total of 3 projection items:
PCA-v1 (1 x 102) idle
PCA-v2 (1 x 102) idle
PCA-v3 (1 x 102) idle
Range : 25800 ... 192599 = 42.956 ... 320.670 secs
Ready.
write_raw_bids(raw,
bids_path=bids_path)
Opening raw data file /home/michael/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
Read a total of 3 projection items:
PCA-v1 (1 x 102) idle
PCA-v2 (1 x 102) idle
PCA-v3 (1 x 102) idle
Range : 25800 ... 192599 = 42.956 ... 320.670 secs
Ready.
Writing '/home/michael/msc_05_example_bids_dataset/README'...
Writing '/home/michael/msc_05_example_bids_dataset/participants.tsv'...
Writing '/home/michael/msc_05_example_bids_dataset/participants.json'...
Writing '/home/michael/msc_05_example_bids_dataset/sub-test/ses-001/meg/sub-test_ses-001_coordsystem.json'...
Writing '/home/michael/msc_05_example_bids_dataset/sub-test/ses-001/meg/sub-test_ses-001_coordsystem.json'...
Writing '/home/michael/msc_05_example_bids_dataset/dataset_description.json'...
Writing '/home/michael/msc_05_example_bids_dataset/sub-test/ses-001/meg/sub-test_ses-001_task-audiovisual_run-001_meg.json'...
Writing '/home/michael/msc_05_example_bids_dataset/sub-test/ses-001/meg/sub-test_ses-001_task-audiovisual_run-001_channels.tsv'...
Copying data files to sub-test_ses-001_task-audiovisual_run-001_meg.fif
Reserving possible split file sub-test_ses-001_task-audiovisual_run-001_split-01_meg.fif
Writing /home/michael/msc_05_example_bids_dataset/sub-test/ses-001/meg/sub-test_ses-001_task-audiovisual_run-001_meg.fif
/tmp/ipykernel_29088/2930231009.py:2: RuntimeWarning: No events found or provided. Please add annotations to the raw data, or provide the events_data and event_id parameters. For resting state data, BIDS recommends naming the task using labels beginning with "rest".
bids_path=bids_path)
Closing /home/michael/msc_05_example_bids_dataset/sub-test/ses-001/meg/sub-test_ses-001_task-audiovisual_run-001_meg.fif
[done]
Writing '/home/michael/msc_05_example_bids_dataset/sub-test/ses-001/sub-test_ses-001_scans.tsv'...
Wrote /home/michael/msc_05_example_bids_dataset/sub-test/ses-001/sub-test_ses-001_scans.tsv entry with meg/sub-test_ses-001_task-audiovisual_run-001_meg.fif.
BIDSPath(
root: /home/michael/msc_05_example_bids_dataset
datatype: meg
basename: sub-test_ses-001_task-audiovisual_run-001_meg.fif)
and let's generate an output path from that
output_path = os.path.join(str(bids_path.root) + (os.sep) + 'sub-test/ses-001/meg')
print(output_path)
/home/michael/msc_05_example_bids_dataset/sub-test/ses-001/meg
Now you could simply use the mne_bids.read_raw_bids() function to import data simply by providing the bids_path.
raw_bids = read_raw_bids(bids_path=bids_path)
Opening raw data file /home/michael/msc_05_example_bids_dataset/sub-test/ses-001/meg/sub-test_ses-001_task-audiovisual_run-001_meg.fif...
Read a total of 3 projection items:
PCA-v1 (1 x 102) idle
PCA-v2 (1 x 102) idle
PCA-v3 (1 x 102) idle
Range : 25800 ... 192599 = 42.956 ... 320.670 secs
Ready.
Reading channel info from /home/michael/msc_05_example_bids_dataset/sub-test/ses-001/meg/sub-test_ses-001_task-audiovisual_run-001_channels.tsv.
/tmp/ipykernel_29088/3789182877.py:1: RuntimeWarning: Did not find any events.tsv associated with sub-test_ses-001_task-audiovisual_run-001.
The search_str was "/home/michael/msc_05_example_bids_dataset/sub-test/**/meg/sub-test_ses-001*events.tsv"
raw_bids = read_raw_bids(bids_path=bids_path)
/tmp/ipykernel_29088/3789182877.py:1: RuntimeWarning: The unit for channel(s) STI 001, STI 002, STI 003, STI 004, STI 005, STI 006, STI 014, STI 015, STI 016 has changed from V to NA.
raw_bids = read_raw_bids(bids_path=bids_path)
raw_bids.info
| Measurement date | December 03, 2002 19:01:10 GMT |
|---|---|
| Experimenter | mne_anonymize | Participant | sub-test |
| Digitized points | 0 points |
| Good channels | 204 Gradiometers, 102 Magnetometers, 9 Stimulus, 60 EEG, 1 EOG |
| Bad channels | MEG 2443, EEG 053 |
| EOG channels | EOG 061 |
| ECG channels | Not available |
| Sampling frequency | 600.61 Hz |
| Highpass | 0.10 Hz |
| Lowpass | 172.18 Hz |
| Projections | PCA-v1 : off PCA-v2 : off PCA-v3 : off |
An important aspect when you're planning on uploading or sharing data with others that we did not have time to adress in this tutorial is annonymization. Consult the MNE-BIDS tutorial: Anonymizing a BIDS dataset for more info.
More info on the BIDS structure will also be provided in the following sessions/chapters.
Saving Data using raw.save()¶
Now that our directory structure is created we can directly save our raw data using the raw.save() function.
This would mostly be used after manipulating the data in some way, but we can also select only a subset of channels or time-points. Let’s say we’re only interested in the contained eeg-channels (+eog-channels for artifact correction later on) for some 30 seconds.
# have to recur a few folder steps due to raw.save using the absolute path of the current notebook as self-reference
save_path = (('..' + str(os.sep) +
'..' + str(os.sep) +
'..' + str(os.sep) +
'..' + str(os.sep) +
'..' + str(os.sep) +
'..' + str(os.sep) +
'..' + str(os.sep) +
'..' + str(os.sep) + '..') + output_path)
# save to fif
raw.save(fname=(save_path + str(os.sep) + 'sub-test_ses-001_eeg_data_cropped.fif'), # the filename
verbose='INFO',
picks=['eeg', 'eog', 'stim'], # the channels to pick (could also be single channels)
tmin=30, # first time-point in seconds included
tmax=60, # last time-point included
overwrite=True) # checks if data is already present and overwrites if true
Writing /home/michael/gitkraken/Cog_Com_Neuro_ML_DL/lecture/introduction/notebooks/neuroscience/../../../../../../../../../home/michael/msc_05_example_bids_dataset/sub-test/ses-001/meg/sub-test_ses-001_eeg_data_cropped.fif
Closing /home/michael/gitkraken/Cog_Com_Neuro_ML_DL/lecture/introduction/notebooks/neuroscience/../../../../../../../../../home/michael/msc_05_example_bids_dataset/sub-test/ses-001/meg/sub-test_ses-001_eeg_data_cropped.fif
[done]
/tmp/ipykernel_29088/187358614.py:18: RuntimeWarning: This filename (/home/michael/gitkraken/Cog_Com_Neuro_ML_DL/lecture/introduction/notebooks/neuroscience/../../../../../../../../../home/michael/msc_05_example_bids_dataset/sub-test/ses-001/meg/sub-test_ses-001_eeg_data_cropped.fif) does not conform to MNE naming conventions. All raw files should end with raw.fif, raw_sss.fif, raw_tsss.fif, _meg.fif, _eeg.fif, _ieeg.fif, raw.fif.gz, raw_sss.fif.gz, raw_tsss.fif.gz, _meg.fif.gz, _eeg.fif.gz or _ieeg.fif.gz
overwrite=True) # checks if data is already present and overwrites if true
Saving data as a csv/tsv using pandas¶
We can further export the data to a pandas DataFrame and save the created DataFrame as a tsv file, if you want the data in a more human readable format or want to do your statistics or plotting with another package. For more info on how to specify the structure and composition of the DataFrame see raw.to_data_frame()
raw_eeg_df = raw.to_data_frame(picks=['eeg', 'eog'], # channels to include
index='time', # set time as index, if not included time will be a separte column
start=30, # first sample to include
stop=60, # first sample to include
long_format=False) # specify format
raw_eeg_df
| channel | EEG 001 | EEG 002 | EEG 003 | EEG 004 | EEG 005 | EEG 006 | EEG 007 | EEG 008 | EEG 009 | EEG 010 | ... | EEG 052 | EEG 053 | EEG 054 | EEG 055 | EEG 056 | EEG 057 | EEG 058 | EEG 059 | EEG 060 | EOG 061 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| time | |||||||||||||||||||||
| 50 | 2.056763e+08 | 3.278046e+08 | 3.167496e+10 | 1.841305e+09 | 5.918540e+08 | 2.018188e+10 | 3.195309e+08 | 6.275372e+08 | 3.995056e+09 | 4.373932e+08 | ... | 4.578265e+08 | 5.495496e+08 | 4.915069e+10 | 6.629004e+08 | 3.542102e+08 | 5.540826e+10 | 7.475891e+08 | 4.301498e+08 | 6.952283e+10 | 2.784705e+09 |
| 52 | 9.974060e+07 | 2.443848e+08 | 2.865830e+10 | 1.855162e+09 | 5.613953e+08 | 2.096741e+10 | 3.507962e+08 | 6.557941e+08 | 2.581421e+09 | 4.729678e+08 | ... | 4.449387e+08 | 5.308362e+08 | 4.920886e+10 | 6.504932e+08 | 3.470544e+08 | 5.529437e+10 | 7.532959e+08 | 4.252548e+08 | 7.016602e+10 | 2.837000e+09 |
| 53 | -6.195068e+05 | 2.966690e+08 | 2.823596e+10 | 1.812435e+09 | 5.793122e+08 | 1.528748e+10 | 3.445432e+08 | 6.387222e+08 | -3.810669e+09 | 4.642200e+08 | ... | 3.682251e+08 | 5.058850e+08 | 4.356671e+10 | 5.884570e+08 | 2.802673e+08 | 5.005536e+10 | 7.019348e+08 | 3.640671e+08 | 6.542981e+10 | 2.863147e+09 |
| 55 | -6.195068e+06 | 4.000626e+08 | 2.914096e+10 | 1.765089e+09 | 6.163404e+08 | 1.196411e+10 | 3.007718e+08 | 6.281259e+08 | -7.682801e+09 | 4.175647e+08 | ... | 3.215833e+08 | 5.027661e+08 | 3.862256e+10 | 5.459180e+08 | 2.319659e+08 | 4.618304e+10 | 6.557098e+08 | 3.187881e+08 | 6.168762e+10 | 2.869684e+09 |
| 57 | 2.230225e+07 | 4.182739e+08 | 2.938229e+10 | 1.761047e+09 | 6.276877e+08 | 1.419983e+10 | 2.301123e+08 | 6.440204e+08 | -1.001837e+10 | 4.018185e+08 | ... | 3.455179e+08 | 4.965283e+08 | 3.990222e+10 | 5.577344e+08 | 2.462775e+08 | 4.783447e+10 | 6.654114e+08 | 3.389801e+08 | 6.373413e+10 | 2.889294e+09 |
| 58 | -1.858521e+06 | 3.654022e+08 | 2.932196e+10 | 1.744880e+09 | 5.882706e+08 | 1.601257e+10 | 1.957205e+08 | 6.381335e+08 | -1.634900e+10 | 4.082336e+08 | ... | 3.608606e+08 | 4.990234e+08 | 4.042572e+10 | 5.612793e+08 | 2.546259e+08 | 4.823309e+10 | 6.722595e+08 | 3.536652e+08 | 6.519592e+10 | 2.908905e+09 |
| 60 | -6.628723e+07 | 3.278046e+08 | 3.046829e+10 | 1.699844e+09 | 5.351172e+08 | 1.498535e+10 | 2.032242e+08 | 6.251825e+08 | -2.495374e+10 | 4.064841e+08 | ... | 3.473590e+08 | 5.208557e+08 | 3.775006e+10 | 5.394190e+08 | 2.421033e+08 | 4.606915e+10 | 6.534271e+08 | 3.346970e+08 | 6.431885e+10 | 2.915442e+09 |
| 62 | -1.164673e+08 | 3.272171e+08 | 3.034763e+10 | 1.646146e+09 | 5.183948e+08 | 1.577087e+10 | 1.975964e+08 | 6.263599e+08 | -2.661322e+10 | 3.802405e+08 | ... | 3.473590e+08 | 5.264697e+08 | 3.629590e+10 | 5.240576e+08 | 2.456812e+08 | 4.487329e+10 | 6.363068e+08 | 3.200119e+08 | 6.414343e+10 | 2.908905e+09 |
| 63 | -1.189453e+08 | 3.272171e+08 | 2.865830e+10 | 1.583788e+09 | 5.309366e+08 | 1.710022e+10 | 1.757108e+08 | 5.757330e+08 | -1.684070e+10 | 3.808237e+08 | ... | 3.387671e+08 | 5.127466e+08 | 3.501623e+10 | 5.057422e+08 | 2.355438e+08 | 4.299408e+10 | 6.140503e+08 | 2.943130e+08 | 6.244776e+10 | 2.889294e+09 |
| 65 | -1.096527e+08 | 3.242798e+08 | 2.974429e+10 | 1.595913e+09 | 5.279505e+08 | 1.734192e+10 | 1.732095e+08 | 4.903738e+08 | -7.867188e+09 | 4.298117e+08 | ... | 3.197421e+08 | 5.214795e+08 | 3.379474e+10 | 4.868359e+08 | 2.051318e+08 | 4.139960e+10 | 6.003540e+08 | 2.716736e+08 | 6.174609e+10 | 2.889294e+09 |
| 67 | -1.815155e+08 | 3.225174e+08 | 3.318329e+10 | 1.664623e+09 | 4.945056e+08 | 1.528748e+10 | 1.869663e+08 | 4.385696e+08 | -7.436951e+09 | 4.624704e+08 | ... | 3.191284e+08 | 5.395691e+08 | 3.373657e+10 | 4.856543e+08 | 1.949945e+08 | 4.236767e+10 | 6.106262e+08 | 2.808517e+08 | 6.396802e+10 | 2.889294e+09 |
| 68 | -2.211639e+08 | 3.377914e+08 | 3.680328e+10 | 1.704463e+09 | 5.082419e+08 | 1.510620e+10 | 2.469955e+08 | 4.462225e+08 | -9.342285e+09 | 4.449747e+08 | ... | 3.412219e+08 | 5.214795e+08 | 3.414374e+10 | 4.992432e+08 | 2.194434e+08 | 4.350659e+10 | 6.243225e+08 | 3.010437e+08 | 6.583911e+10 | 2.850073e+09 |
| 70 | -1.226624e+08 | 3.554153e+08 | 3.764795e+10 | 1.714279e+09 | 5.781177e+08 | 1.915466e+10 | 3.101514e+08 | 5.145099e+08 | -7.068176e+09 | 4.327277e+08 | ... | 3.547235e+08 | 4.952808e+08 | 3.286407e+10 | 5.021973e+08 | 2.355438e+08 | 4.202600e+10 | 6.180450e+08 | 3.022675e+08 | 6.478662e+10 | 2.810852e+09 |
| 72 | -2.787781e+07 | 3.336792e+08 | 3.378662e+10 | 1.669242e+09 | 5.691593e+08 | 1.764404e+10 | 2.820126e+08 | 5.580725e+08 | -6.699402e+09 | 4.484738e+08 | ... | 3.283341e+08 | 4.965283e+08 | 2.861792e+10 | 4.697022e+08 | 2.033429e+08 | 3.724255e+10 | 5.746735e+08 | 2.655548e+08 | 6.016736e+10 | 2.830463e+09 |
| 73 | -6.195068e+06 | 3.025436e+08 | 3.125263e+10 | 1.548567e+09 | 4.879361e+08 | 1.142029e+10 | 2.432437e+08 | 4.727133e+08 | -7.436951e+09 | 4.671359e+08 | ... | 3.043994e+08 | 5.033899e+08 | 2.600043e+10 | 4.454785e+08 | 1.723346e+08 | 3.456610e+10 | 5.449982e+08 | 2.398560e+08 | 5.730225e+10 | 2.902368e+09 |
| 75 | -2.292175e+07 | 2.896194e+08 | 3.342462e+10 | 1.408838e+09 | 4.377689e+08 | 1.045349e+10 | 2.776355e+08 | 3.284857e+08 | -3.995056e+09 | 4.776334e+08 | ... | 3.037857e+08 | 5.108752e+08 | 2.733826e+10 | 4.454785e+08 | 1.765088e+08 | 3.581891e+10 | 5.546997e+08 | 2.484222e+08 | 5.759461e+10 | 2.993884e+09 |
| 77 | -8.735047e+07 | 2.837448e+08 | 3.487262e+10 | 1.340706e+09 | 4.652414e+08 | 1.522705e+10 | 3.070248e+08 | 2.849231e+08 | -1.229248e+09 | 4.723846e+08 | ... | 2.945801e+08 | 5.320837e+08 | 2.885059e+10 | 4.442969e+08 | 1.747199e+08 | 3.673004e+10 | 5.626892e+08 | 2.545410e+08 | 5.689295e+10 | 3.026569e+09 |
| 78 | -1.579742e+08 | 2.802200e+08 | 3.089063e+10 | 1.399600e+09 | 5.201865e+08 | 1.546875e+10 | 2.613776e+08 | 3.738144e+08 | -4.240906e+09 | 4.718015e+08 | ... | 2.792374e+08 | 5.345789e+08 | 2.879242e+10 | 4.389795e+08 | 1.604083e+08 | 3.627447e+10 | 5.564117e+08 | 2.484222e+08 | 5.537268e+10 | 2.980811e+09 |
| 80 | -1.319550e+08 | 2.931442e+08 | 2.678796e+10 | 1.549144e+09 | 5.673676e+08 | 1.099731e+10 | 1.475720e+08 | 4.880191e+08 | -1.100177e+10 | 4.484738e+08 | ... | 2.933527e+08 | 5.127466e+08 | 3.053741e+10 | 4.590674e+08 | 1.765088e+08 | 3.792590e+10 | 5.689667e+08 | 2.606598e+08 | 5.700989e+10 | 2.908905e+09 |
| 82 | -6.071167e+07 | 3.066559e+08 | 2.648630e+10 | 1.655962e+09 | 6.026041e+08 | 8.701172e+09 | 6.628235e+07 | 5.174533e+08 | -1.259979e+10 | 4.158151e+08 | ... | 3.264929e+08 | 5.133704e+08 | 3.356207e+10 | 4.909717e+08 | 2.134802e+08 | 4.111487e+10 | 5.975006e+08 | 2.869705e+08 | 6.051819e+10 | 2.876221e+09 |
| 83 | -1.288574e+08 | 2.813950e+08 | 2.769296e+10 | 1.685409e+09 | 6.032013e+08 | 7.794800e+09 | 7.378601e+07 | 4.562302e+08 | -9.833985e+09 | 3.983194e+08 | ... | 3.283341e+08 | 5.314600e+08 | 3.338757e+10 | 4.903809e+08 | 2.170581e+08 | 4.111487e+10 | 6.014954e+08 | 2.875824e+08 | 6.098596e+10 | 2.889294e+09 |
| 85 | -3.091339e+08 | 2.332230e+08 | 2.877896e+10 | 1.712546e+09 | 5.894650e+08 | 5.982055e+09 | 1.294382e+08 | 3.879428e+08 | -9.403748e+09 | 3.761581e+08 | ... | 2.976486e+08 | 5.152417e+08 | 3.030475e+10 | 4.643848e+08 | 1.896277e+08 | 3.821063e+10 | 5.775268e+08 | 2.631073e+08 | 5.847168e+10 | 2.921979e+09 |
| 87 | -3.605530e+08 | 2.056122e+08 | 2.811530e+10 | 1.681945e+09 | 5.697565e+08 | 2.054443e+09 | 1.782120e+08 | 3.955957e+08 | -1.757825e+10 | 3.417499e+08 | ... | 2.810785e+08 | 4.877954e+08 | 2.885059e+10 | 4.555225e+08 | 1.842609e+08 | 3.718561e+10 | 5.689667e+08 | 2.563766e+08 | 5.782849e+10 | 2.961200e+09 |
| 88 | -2.360321e+08 | 2.220612e+08 | 2.521930e+10 | 1.575127e+09 | 5.243671e+08 | -6.223755e+09 | 1.888422e+08 | 4.438678e+08 | -2.495374e+10 | 3.172559e+08 | ... | 3.013309e+08 | 4.884192e+08 | 3.088641e+10 | 4.791553e+08 | 2.099023e+08 | 3.991901e+10 | 5.929352e+08 | 2.808517e+08 | 6.063513e+10 | 2.941589e+09 |
| 90 | -8.982849e+07 | 2.778702e+08 | 2.479697e+10 | 1.557228e+09 | 5.160059e+08 | -5.740356e+09 | 1.969711e+08 | 5.121551e+08 | -1.905335e+10 | 3.423331e+08 | ... | 3.510413e+08 | 4.940332e+08 | 3.588873e+10 | 5.317383e+08 | 2.570111e+08 | 4.504413e+10 | 6.437256e+08 | 3.291901e+08 | 6.554675e+10 | 2.908905e+09 |
| 92 | -1.734619e+07 | 3.419037e+08 | 2.678796e+10 | 1.638063e+09 | 5.446729e+08 | 7.673950e+09 | 2.094772e+08 | 5.898614e+08 | -9.956909e+09 | 3.773245e+08 | ... | 3.768170e+08 | 5.008948e+08 | 3.966955e+10 | 5.665967e+08 | 2.880194e+08 | 4.863171e+10 | 6.773956e+08 | 3.622314e+08 | 6.847034e+10 | 2.895831e+09 |
| 93 | -1.796570e+07 | 3.466034e+08 | 2.419363e+10 | 1.676171e+09 | 5.088391e+08 | 1.395813e+10 | 1.769614e+08 | 5.992804e+08 | -1.143201e+10 | 3.475818e+08 | ... | 3.436768e+08 | 5.233508e+08 | 3.804089e+10 | 5.423731e+08 | 2.641669e+08 | 4.720807e+10 | 6.608459e+08 | 3.414276e+08 | 6.560523e+10 | 2.908905e+09 |
| 95 | -6.318970e+07 | 2.984314e+08 | 2.015131e+10 | 1.700999e+09 | 4.138797e+08 | 8.278198e+09 | 1.444455e+08 | 5.645480e+08 | -1.757825e+10 | 3.143399e+08 | ... | 3.289478e+08 | 5.302124e+08 | 3.647040e+10 | 5.175586e+08 | 2.426996e+08 | 4.595526e+10 | 6.505737e+08 | 3.230713e+08 | 6.332483e+10 | 2.928516e+09 |
| 97 | -1.022186e+08 | 2.631836e+08 | 1.990997e+10 | 1.747190e+09 | 3.965601e+08 | 7.250976e+09 | 1.531998e+08 | 5.727896e+08 | -1.813141e+10 | 3.487482e+08 | ... | 3.627017e+08 | 5.196082e+08 | 4.019305e+10 | 5.429639e+08 | 2.677448e+08 | 4.954285e+10 | 6.848144e+08 | 3.506058e+08 | 6.724243e+10 | 2.967737e+09 |
| 98 | -1.071747e+08 | 2.614212e+08 | 2.238364e+10 | 1.754696e+09 | 4.646442e+08 | 1.087646e+10 | 1.688324e+08 | 5.992804e+08 | -1.505829e+10 | 3.819901e+08 | ... | 3.854089e+08 | 5.264697e+08 | 4.461371e+10 | 5.677783e+08 | 2.957715e+08 | 5.301654e+10 | 7.104950e+08 | 3.805878e+08 | 7.092615e+10 | 2.993884e+09 |
30 rows × 61 columns
For some analysis is preferable to have data organised in long format, i.e. so that everey sample for every channel is one row. This can be achieved by setting the long_format parameter to "True".
raw_eeg_df = raw.to_data_frame(picks=['eeg', 'eog', 'stim'], # channels to include
index='time', # set time as index, if not included time will be a separte column
start=30, # first sample to include
stop=60, # first sample to include
long_format=True) # specify format
Converting "channel" to "category"...
Converting "ch_type" to "category"...
raw_eeg_df
| channel | ch_type | value | |
|---|---|---|---|
| time | |||
| 50 | STI 001 | stim | 0.000000e+00 |
| 50 | STI 002 | stim | 0.000000e+00 |
| 50 | STI 003 | stim | 0.000000e+00 |
| 50 | STI 004 | stim | 0.000000e+00 |
| 50 | STI 005 | stim | 0.000000e+00 |
| ... | ... | ... | ... |
| 98 | EEG 057 | eeg | 5.301654e+10 |
| 98 | EEG 058 | eeg | 7.104950e+08 |
| 98 | EEG 059 | eeg | 3.805878e+08 |
| 98 | EEG 060 | eeg | 7.092615e+10 |
| 98 | EOG 061 | eog | 2.993884e+09 |
2100 rows × 3 columns
To save our DataFrame, we can now simply use the pands fucntion pd.DataFrame.to_csv()
fname = output_path + str(os.sep) + 'sub-test_ses-001_eeg_data_cropped.tsv'
raw_eeg_df.to_csv(fname, sep='\t')
Further reading¶
For some more in-depth explanations on how to work with raw data, such as how to drop or rename channels, see MNE tutorials: The Raw data structure: continuous data .
For an overview of the general workflow using MNE, see the MNE cookbook.
Michael Ernst
Phd student - Fiebach Lab, Neurocognitive Psychology at Goethe-University Frankfurt