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Hello JR,
Thanks!
you can use scipy.io.load_mat to read a mat file, and then put the
corresponding information into an mne object:
This looks pretty clear;
e.g.
import mne
from scipy.io<http://scipy.io> import load_mat
info = mne.create_info(32, sfreq=256., ch_types='eeg') # 32 EEG channels
sampled at 256Hz
mat = load_mat('data.mat') # matrix of32 channels x n time samples
raw = mne.RawArray(mat, info=info)
or
mat = load_mat('data.mat')
epochs = mne.EpochsArray(mat, info=info, tmin=-.500) # first time sample @
-500ms
Here I have one questions;
Right now, I was working with math files that were carrying information about each trial and sensory (i.e., 102 mag sensors x 84 correct trials in a specific condition)
Therefore, my .mat file right now is 102 (sensors) x 84 (in that specific condition) x 1150 time points (2ms bins).
If what I have to give MNE is ONE ? mat = load_mat('data.mat') # matrix of 32 channels x n time samples ?, then I guess that I??ll need to work on weighted average files for each task condiiton
This way, my matrix will be 102 x 84 (one per each of the four conditions).
Am I correct?
Please, let me know if any additional clarification is needed;
Best,
For the anova you can check
https://martinos.org/mne/stable/auto_tutorials/plot_background_statistics.html?highlight=anova
best regards,
JR
Alfredo Spagna, Ph. D.
alfredo.spagna at icm-institute.org<mailto:alfredo.spagna at icm-institute.org>
PICNIC Lab @ Institut du Cerveau
et de la Moelle e?pinie?re
Inserm U 1127, Paris, France