linear regression analysis of ERP data. Neuroimage, 2006
"""
# Authors: Tal Linzen <*****@*****.**>
# Denis A. Engemann <*****@*****.**>
# Jona Sassenhagen <*****@*****.**>
#
# License: BSD (3-clause)
import pandas as pd
import mne
from mne.stats import linear_regression, fdr_correction
from mne.viz import plot_compare_evokeds
from mne.datasets import kiloword
# Load the data
path = kiloword.data_path() + '/kword_metadata-epo.fif'
epochs = mne.read_epochs(path)
print(epochs.metadata.head())
##############################################################################
# Psycholinguistically relevant word characteristics are continuous. I.e.,
# concreteness or imaginability is a graded property. In the metadata,
# we have concreteness ratings on a 5-point scale. We can show the dependence
# of the EEG on concreteness by dividing the data into bins and plotting the
# mean activity per bin, color coded.
name = "Concreteness"
df = epochs.metadata
df[name] = pd.cut(df[name], 11, labels=False) / 10
colors = {str(val): val for val in df[name].unique()}
epochs.metadata = df.assign(Intercept=1) # Add an intercept for later
evokeds = {val: epochs[name + " == " + val].average() for val in colors}
linear regression analysis of ERP data. Neuroimage, 2006
"""
# Authors: Tal Linzen <*****@*****.**>
# Denis A. Engemann <*****@*****.**>
# Jona Sassenhagen <*****@*****.**>
#
# License: BSD (3-clause)
import pandas as pd
import mne
from mne.stats import linear_regression, fdr_correction
from mne.viz import plot_compare_evokeds
from mne.datasets import kiloword
# Load the data
path = kiloword.data_path() + '/kword_metadata-epo.fif'
epochs = mne.read_epochs(path)
print(epochs.metadata.head())
##############################################################################
# Psycholinguistically relevant word characteristics are continuous. I.e.,
# concreteness or imaginability is a graded property. In the metadata,
# we have concreteness ratings on a 5-point scale. We can show the dependence
# of the EEG on concreteness by dividing the data into bins and plotting the
# mean activity per bin, color coded.
name = "Concreteness"
df = epochs.metadata
df[name] = pd.cut(df[name], 11, labels=False) / 10
colors = {str(val): val for val in df[name].unique()}
epochs.metadata = df.assign(Intercept=1) # Add an intercept for later
evokeds = {val: epochs[name + " == " + val].average() for val in colors}