"""
============================================================================
Analysing continuous features with binning and regression in sensor space
============================================================================
Predict single trial activity from a continuous variable.
A single-trial regression is performed in each sensor and timepoint
individually, resulting in an :class:`mne.Evoked` object which contains the
regression coefficient (beta value) for each combination of sensor and
timepoint. This example shows the regression coefficient; the t and p values
are also calculated automatically.
Here, we repeat a few of the analyses from [1]_. This can be easily performed
by accessing the metadata object, which contains word-level information about
various psycholinguistically relevant features of the words for which we have
EEG activity.
For the general methodology, see e.g. [2]_.
References
----------
.. [1] Dufau, S., Grainger, J., Midgley, KJ., Holcomb, PJ. A thousand
words are worth a picture: Snapshots of printed-word processing in an
event-related potential megastudy. Psychological Science, 2015
.. [2] Hauk et al. The time course of visual word recognition as revealed by
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}
plot_compare_evokeds(evokeds, colors=colors, split_legend=True,
cmap=(name + " Percentile", "viridis"))
##############################################################################
# We observe that there appears to be a monotonic dependence of EEG on
# concreteness. We can also conduct a continuous analysis: single-trial level
# regression with concreteness as a continuous (although here, binned)
# feature. We can plot the resulting regression coefficient just like an
# Event-related Potential.
names = ["Intercept", name]
res = linear_regression(epochs, epochs.metadata[names], names=names)
for cond in names:
res[cond].beta.plot_joint(title=cond, ts_args=dict(time_unit='s'),
topomap_args=dict(time_unit='s'))
##############################################################################
# Because the `linear_regression` function also estimates p values, we can --
# after applying FDR correction for multiple comparisons -- also visualise the
# statistical significance of the regression of word concreteness.
# The :func:`mne.viz.plot_evoked_image` function takes a `mask` parameter.
# If we supply it with a boolean mask of the positions where we can reject
# the null hypothesis, points that are not significant will be shown
# transparently, and if desired, in a different colour palette and surrounded
# by dark contour lines.
reject_H0, fdr_pvals = fdr_correction(res["Concreteness"].p_val.data)
evoked = res["Concreteness"].beta
evoked.plot_image(mask=reject_H0, time_unit='s')