Note
Click here to download the full example code
The Info data structure#
This tutorial describes the mne.Info data structure, which keeps track
of various recording details, and is attached to Raw,
Epochs, and Evoked objects.
We will begin by loading the Python modules we need, and loading the same example data we used in the introductory tutorial:
import os
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
Read a total of 4 projection items:
PCA-v1 (1 x 102) idle
PCA-v2 (1 x 102) idle
PCA-v3 (1 x 102) idle
Average EEG reference (1 x 60) idle
Range : 6450 ... 48149 = 42.956 ... 320.665 secs
Ready.
As seen in the introductory tutorial, when a
Raw object is loaded, an Info object is
created automatically, and stored in the raw.info attribute:
print(raw.info)
<Info | 15 non-empty values
bads: 2 items (MEG 2443, EEG 053)
ch_names: MEG 0113, MEG 0112, MEG 0111, MEG 0122, MEG 0123, MEG 0121, MEG ...
chs: 204 Gradiometers, 102 Magnetometers, 9 Stimulus, 60 EEG, 1 EOG
custom_ref_applied: False
dev_head_t: MEG device -> head transform
dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra)
file_id: 4 items (dict)
highpass: 0.1 Hz
hpi_meas: 1 item (list)
hpi_results: 1 item (list)
lowpass: 40.0 Hz
meas_date: 2002-12-03 19:01:10 UTC
meas_id: 4 items (dict)
nchan: 376
projs: PCA-v1: off, PCA-v2: off, PCA-v3: off, Average EEG reference: off
sfreq: 150.2 Hz
>
However, it is not strictly necessary to load the Raw object
in order to view or edit the Info object; you can extract all
the relevant information into a stand-alone Info object using
mne.io.read_info():
info = mne.io.read_info(sample_data_raw_file)
print(info)
Read a total of 4 projection items:
PCA-v1 (1 x 102) idle
PCA-v2 (1 x 102) idle
PCA-v3 (1 x 102) idle
Average EEG reference (1 x 60) idle
<Info | 15 non-empty values
bads: 2 items (MEG 2443, EEG 053)
ch_names: MEG 0113, MEG 0112, MEG 0111, MEG 0122, MEG 0123, MEG 0121, MEG ...
chs: 204 Gradiometers, 102 Magnetometers, 9 Stimulus, 60 EEG, 1 EOG
custom_ref_applied: False
dev_head_t: MEG device -> head transform
dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra)
file_id: 4 items (dict)
highpass: 0.1 Hz
hpi_meas: 1 item (list)
hpi_results: 1 item (list)
lowpass: 40.0 Hz
meas_date: 2002-12-03 19:01:10 UTC
meas_id: 4 items (dict)
nchan: 376
projs: PCA-v1: off, PCA-v2: off, PCA-v3: off, Average EEG reference: off
sfreq: 150.2 Hz
>
As you can see, the Info object keeps track of a lot of
information about:
the recording system (gantry angle, HPI details, sensor digitizations, channel names, …)
the experiment (project name and ID, subject information, recording date, experimenter name or ID, …)
the data (sampling frequency, applied filter frequencies, bad channels, projectors, …)
The complete list of fields is given in the API documentation.
Querying the Info object#
The fields in a Info object act like Python dictionary keys, using square brackets and strings to access the contents of a
field:
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'])
['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']
Most of the fields contain int, float, or list
data, but the chs field bears special mention: it contains a list of
dictionaries (one dict per channel) containing everything there is
to know about a channel other than the data it recorded. Normally it is not
necessary to dig into the details of the chs field — various MNE-Python
functions can extract the information more cleanly than iterating over the
list of dicts yourself — but it can be helpful to know what is in there. Here
we show the keys for the first channel’s dict:
print(info['chs'][0].keys())
dict_keys(['scanno', 'logno', 'kind', 'range', 'cal', 'coil_type', 'loc', 'unit', 'unit_mul', 'ch_name', 'coord_frame'])
Obtaining subsets of channels#
It is often useful to convert between channel names and the integer indices
identifying rows of the data array where those channels’ measurements are
stored. The Info object is useful for this task; two
convenience functions that rely on the mne.Info object for picking
channels are mne.pick_channels() and mne.pick_types().
pick_channels() minimally takes a list of all channel names and a
list of channel names to include; it is also possible to provide an empty
list to include and specify which channels to exclude instead:
print(mne.pick_channels(info['ch_names'], include=['MEG 0312', 'EEG 005']))
print(mne.pick_channels(info['ch_names'], include=[],
exclude=['MEG 0312', 'EEG 005']))
[ 25 319]
[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
18 19 20 21 22 23 24 26 27 28 29 30 31 32 33 34 35 36
37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252
253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270
271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306
307 308 309 310 311 312 313 314 315 316 317 318 320 321 322 323 324 325
326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343
344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361
362 363 364 365 366 367 368 369 370 371 372 373 374 375]
pick_types() works differently, since channel type cannot always
be reliably determined from channel name alone. Consequently,
pick_types() needs an Info object instead of just a
list of channel names, and has boolean keyword arguments for each channel
type. Default behavior is to pick only MEG channels (and MEG reference
channels if present) and exclude any channels already marked as “bad” in the
bads field of the Info object. Therefore, to get all and
only the EEG channel indices (including the “bad” EEG channels) we must
pass meg=False and exclude=[]:
print(mne.pick_types(info, meg=False, eeg=True, exclude=[]))
[315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332
333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350
351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
369 370 371 372 373 374]
Note that the meg and fnirs parameters of pick_types()
accept strings as well as boolean values, to allow selecting only
magnetometer or gradiometer channels (via meg='mag' or meg='grad') or
to pick only oxyhemoglobin or deoxyhemoglobin channels (via fnirs='hbo'
or fnirs='hbr', respectively).
A third way to pick channels from an Info object is to apply
regular expression matching to the channel names using
mne.pick_channels_regexp(). Here the ^ represents the beginning of
the string and . character matches any single character, so both EEG and
EOG channels will be selected:
print(mne.pick_channels_regexp(info['ch_names'], '^E.G'))
[315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375]
pick_channels_regexp() can be especially useful for channels named
according to the 10-20 system (e.g., to select all channels
ending in “z” to get the midline, or all channels beginning with “O” to get
the occipital channels). Note that pick_channels_regexp() uses the
Python standard module re to perform regular expression matching; see
the documentation of the re module for implementation details.
Warning
Both pick_channels() and pick_channels_regexp()
operate on lists of channel names, so they are unaware of which channels
(if any) have been marked as “bad” in info['bads']. Use caution to
avoid accidentally selecting bad channels.
Obtaining channel type information#
Sometimes it can be useful to know channel type based on its index in the
data array. For this case, use mne.channel_type(), which takes
an Info object and a single integer channel index:
print(mne.channel_type(info, 25))
grad
To obtain several channel types at once, you could embed
channel_type() in a list comprehension, or use the
get_channel_types() method of a Raw,
Epochs, or Evoked instance:
picks = (25, 76, 77, 319)
print([mne.channel_type(info, x) for x in picks])
print(raw.get_channel_types(picks=picks))
['grad', 'grad', 'mag', 'eeg']
['grad', 'grad', 'mag', 'eeg']
Alternatively, you can get the indices of all channels of all channel types
present in the data, using channel_indices_by_type(),
which returns a dict with channel types as keys, and lists of
channel indices as values:
ch_idx_by_type = mne.channel_indices_by_type(info)
print(ch_idx_by_type.keys())
print(ch_idx_by_type['eog'])
dict_keys(['eeg', 'csd', 'stim', 'eog', 'ecg', 'emg', 'misc', 'resp', 'chpi', 'exci', 'ias', 'syst', 'seeg', 'dipole', 'gof', 'bio', 'ecog', 'dbs', 'ref_meg', 'mag', 'grad', 'hbo', 'hbr', 'fnirs_cw_amplitude', 'fnirs_fd_ac_amplitude', 'fnirs_fd_phase', 'fnirs_od'])
[375]
Dropping channels from an Info object#
If you want to modify an Info object by eliminating some of the
channels in it, you can use the mne.pick_info() function to pick the
channels you want to keep and omit the rest:
print(info['nchan'])
eeg_indices = mne.pick_types(info, meg=False, eeg=True)
print(mne.pick_info(info, eeg_indices)['nchan'])
376
59
We can also get a nice HTML representation in IPython like:
By default, pick_info() will make a copy of the original
Info object before modifying it; if you want to modify it
in-place, include the parameter copy=False.
Total running time of the script: ( 0 minutes 6.033 seconds)
Estimated memory usage: 9 MB