Exemplo n.º 1
0
# Authors: Jose C. Garcia Alanis <*****@*****.**>
#
# License: BSD (3-clause)

import numpy as np
import matplotlib.pyplot as plt

import mne
from mne.datasets import limo
from mne.stats import linear_regression

print(__doc__)

# fetch data from subject 2 and interpolate missing channels
limo_epochs = limo.load_data(subject=2)

###############################################################################
# In the original LIMO experiment, participants performed a two-alternative
# forced choice task discriminating between the same two faces.
# The critical manipulation in the experiment was that the phase-coherence of
# the presented face-stimuli was varied across a noise-signal continuum
# spanning from 0 to 100 %. In other words, faces with high phase coherence
# were easily discernible, while faces with low phase-coherence were hard to
# identify as such).
# The events coding the presentation of each of these two faces are stored in
# ``limo_epochs.events``.
#
# We can visualise the distribution of the face events contained in the
# epochs structure. Events should appear clearly grouped, as they are ordered
# during the import process.
Exemplo n.º 2
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from mne.datasets import limo
from mne.evoked import EvokedArray
from mne.stats import fdr_correction
from mne.viz import plot_compare_evokeds

###############################################################################
# Here, we'll import only one subject. The example shows how to compute p-values
# for  beta coefficients derived from linear regression using sklearn.
# In addition, we propose to visualize these p-values in terms of
# Shannon information values [1]_ (i.e., surprise values)
# for better interpretation.

# subject id
subjects = [2]
# create a dictionary containing participants data
limo_epochs = {str(subj): limo.load_data(subject=subj) for subj in subjects}

# interpolate missing channels
for subject in limo_epochs.values():
    subject.interpolate_bads(reset_bads=True)

# epochs to use for analysis
epochs = limo_epochs['2']

# only keep eeg channels
epochs = epochs.pick_types(eeg=True)

# save epochs information (needed for creating a homologous
# epochs object containing linear regression result)
epochs_info = epochs.info
tmin = epochs.tmin
Exemplo n.º 3
0
#
# The presented faces varied across a noise-signal (or phase-coherence)
# continuum spanning from 0 to 85% in increasing steps of 5%.
# In other words, faces with high phase-coherence (e.g., 85%) were easy to
# identify, while faces with low phase-coherence (e.g., 5%) were hard to
# identify and by extension very hard to discriminate.
#
#
# Load the data
# -------------
#
# We'll begin by loading the data from subject 1 of the LIMO dataset.

# This step can take a little while if you're loading the data for the
# first time.
limo_epochs = load_data(subject=subj)

###############################################################################
# Note that the result of the loading process is an
# :class:`mne.EpochsArray` containing the data ready to interface
# with MNE-Python.

print(limo_epochs)

###############################################################################
# Visualize events
# ----------------
#
# We can visualise the distribution of the face events contained in the
# ``limo_epochs`` structure. Events should appear clearly grouped, as the
# epochs are ordered by condition.