@Alexandre
Would there be a way to test that these two Matlab functions still work after MNE-Python updates?
- brainstorm3/toolbox/io/out_mne_data.m at master · brainstorm-tools/brainstorm3 · GitHub
- brainstorm3/toolbox/io/out_mne_channel.m at master · brainstorm-tools/brainstorm3 · GitHub
And possibly some hints on keeping this tutorial page up-to-date?
https://neuroimage.usc.edu/brainstorm/MnePython
Example files:
channel.zip (1.2 MB)
data.zip (892.0 KB)
Example out_mne_channel:
>> out_mne_channel('/path/to/channel.mat')
MNE> Fix CTF/Brainstorm=>Neuromag transformations
Quaternion matching (desired vs. transformed):
1.75 67.92 -0.00 mm <-> 1.75 67.92 -0.00 mm (orig : -52.45 45.43 -264.57 mm) diff = 0.000 mm
-1.75 -67.92 0.00 mm <-> -1.75 -67.92 -0.00 mm (orig : 46.65 -47.54 -265.69 mm) diff = 0.000 mm
106.07 0.00 -0.00 mm <-> 106.07 0.00 -0.00 mm (orig : 65.76 75.83 -240.13 mm) diff = 0.000 mm
Coordinate transformations established.
TODO: Export CTF compensators
ans =
Python Info with properties:
ch_names: [1×340 py.list]
compensation_grade: [1×1 py.int]
<Info | 12 non-empty values
bads: []
ch_names: UDIO001, UPPT001, UTRG001, SCLK01, BG1, BG2, BG3, BP1, BP2, BP3, ...
chs: 3 Stimulus, 34 misc, 26 Reference Magnetometers, 274 Magnetometers, 1 ECG, 2 EOG
ctf_head_t: CTF/4D/KIT head -> head transform
custom_ref_applied: False
dev_ctf_t: MEG device -> CTF/4D/KIT head transform
dev_head_t: MEG device -> head transform
dig: 253 items (6 Cardinal, 6 HPI, 241 Extra)
highpass: 0.0 Hz
lowpass: 500.0 Hz
meas_date: unspecified
nchan: 340
projs: cardiac: SSP_pca, MEG, 07-Nov-2021 14:31:03: off, blink: ...
sfreq: 1000.0 Hz
>
Example out_mne_data (Raw):
>> out_mne_data('/path/to/data.mat', 'Raw', '/path/to/channel.mat')
Warning: File not found in database: /path/to/data.mat
> In out_mne_data (line 71)
MNE> Fix CTF/Brainstorm=>Neuromag transformations
Quaternion matching (desired vs. transformed):
1.75 67.92 -0.00 mm <-> 1.75 67.92 -0.00 mm (orig : -52.45 45.43 -264.57 mm) diff = 0.000 mm
-1.75 -67.92 0.00 mm <-> -1.75 -67.92 -0.00 mm (orig : 46.65 -47.54 -265.69 mm) diff = 0.000 mm
106.07 0.00 -0.00 mm <-> 106.07 0.00 -0.00 mm (orig : 65.76 75.83 -240.13 mm) diff = 0.000 mm
Coordinate transformations established.
TODO: Export CTF compensators
Creating RawArray with float64 data, n_channels=340, n_times=361
Range : -60 ... 300 = -0.100 ... 0.500 secs
Ready.
ans =
C:\Users\franc\AppData\Local\Programs\Python\Python38\lib\site-packages\mne\utils\_logging.py:493: DeprecationWarning: The verbose class attribute has been deprecated in 1.0 and will be removed in 1.1, pass verbose to methods as required to change log levels instead
warn('The verbose class attribute has been deprecated in 1.0 and will '
Python RawArray with properties:
annotations: [1×1 py.mne.annotations.Annotations]
ch_names: [1×340 py.list]
compensation_grade: [1×1 py.int]
filenames: [1×1 py.tuple]
first_samp: [1×1 py.int]
first_time: -0.1000
last_samp: [1×1 py.numpy.int32]
n_times: [1×1 py.numpy.int32]
proj: 1
times: [1×1 py.numpy.ndarray]
verbose: [1×1 py.NoneType]
preload: 1
orig_format: [1×6 py.str]
info: [1×1 py.mne.io.meas_info.Info]
buffer_size_sec: 1
<RawArray | 340 x 361 (0.6 s), ~1.4 MB, data loaded>
Example out_mne_data (Evoked):
>> out_mne_data('/path/to/data.mat', 'Evoked', '/path/to/channel.mat')
Warning: File not found in database: /path/to/data.mat
> In out_mne_data (line 71)
MNE> Fix CTF/Brainstorm=>Neuromag transformations
Quaternion matching (desired vs. transformed):
1.75 67.92 -0.00 mm <-> 1.75 67.92 -0.00 mm (orig : -52.45 45.43 -264.57 mm) diff = 0.000 mm
-1.75 -67.92 0.00 mm <-> -1.75 -67.92 -0.00 mm (orig : 46.65 -47.54 -265.69 mm) diff = 0.000 mm
106.07 0.00 -0.00 mm <-> 106.07 0.00 -0.00 mm (orig : 65.76 75.83 -240.13 mm) diff = 0.000 mm
Coordinate transformations established.
TODO: Export CTF compensators
ans =
C:\Users\franc\AppData\Local\Programs\Python\Python38\lib\site-packages\mne\utils\_logging.py:493: DeprecationWarning: The verbose class attribute has been deprecated in 1.0 and will be removed in 1.1, pass verbose to methods as required to change log levels instead
warn('The verbose class attribute has been deprecated in 1.0 and will '
Python EvokedArray with properties:
ch_names: [1×340 py.list]
compensation_grade: [1×1 py.int]
data: [1×1 py.numpy.ndarray]
kind: [1×7 py.str]
proj: 1
tmax: 0.5000
tmin: -0.1000
verbose: [1×1 py.NoneType]
comment: [1×23 py.str]
last: [1×1 py.int]
times: [1×1 py.numpy.ndarray]
first: [1×1 py.int]
baseline: [1×1 py.NoneType]
picks: [1×1 py.NoneType]
preload: 1
nave: [1×1 py.int]
info: [1×1 py.mne.io.meas_info.Info]
<Evoked | 'Avg: deviant (39 files)' (average, N=39), -0.1 – 0.5 sec, baseline off, 340 ch, ~1.4 MB>
Example out_mne_data (Epoched):
>> out_mne_data('/path/to/data.mat', 'Epoched', '/path/to/channel.mat')
Warning: File not found in database: /path/to/data.mat
> In out_mne_data (line 71)
MNE> Fix CTF/Brainstorm=>Neuromag transformations
Quaternion matching (desired vs. transformed):
1.75 67.92 -0.00 mm <-> 1.75 67.92 -0.00 mm (orig : -52.45 45.43 -264.57 mm) diff = 0.000 mm
-1.75 -67.92 0.00 mm <-> -1.75 -67.92 -0.00 mm (orig : 46.65 -47.54 -265.69 mm) diff = 0.000 mm
106.07 0.00 -0.00 mm <-> 106.07 0.00 -0.00 mm (orig : 65.76 75.83 -240.13 mm) diff = 0.000 mm
Coordinate transformations established.
TODO: Export CTF compensators
Not setting metadata
1 matching events found
No baseline correction applied
Created an SSP operator (subspace dimension = 3)
3 projection items activated
0 bad epochs dropped
ans =
C:\Users\franc\AppData\Local\Programs\Python\Python38\lib\site-packages\mne\utils\_logging.py:493: DeprecationWarning: The verbose class attribute has been deprecated in 1.0 and will be removed in 1.1, pass verbose to methods as required to change log levels instead
warn('The verbose class attribute has been deprecated in 1.0 and will '
Python EpochsArray with properties:
annotations: [1×1 py.NoneType]
ch_names: [1×340 py.list]
compensation_grade: [1×1 py.int]
filename: [1×1 py.NoneType]
metadata: [1×1 py.NoneType]
proj: 1
times: [1×1 py.numpy.ndarray]
tmax: 0.5000
tmin: -0.1000
verbose: [1×1 py.NoneType]
drop_log: [1×1 py.tuple]
baseline: [1×1 py.NoneType]
picks: [1×1 py.numpy.ndarray]
preload: 1
flat: [1×1 py.NoneType]
selection: [1×1 py.numpy.ndarray]
reject: [1×1 py.NoneType]
detrend: [1×1 py.NoneType]
reject_tmax: [1×1 py.NoneType]
event_id: [1×1 py.dict]
info: [1×1 py.mne.io.meas_info.Info]
reject_tmin: [1×1 py.NoneType]
events: [1×1 py.numpy.ndarray]
<EpochsArray | 1 events (all good), -0.1 - 0.5 sec, baseline off, ~1.4 MB, data loaded,
'Avg: deviant': 1>