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%matplotlib inline

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



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# Authors: Tal Linzen <linzen@nyu.edu>
#          Denis A. Engemann <denis.engemann@gmail.com>
#          Jona Sassenhagen <jona.sassenhagen@gmail.com>
#
# 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.



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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.



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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 :func:~mne.stats.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.



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reject_H0, fdr_pvals = fdr_correction(res["Concreteness"].p_val.data)
evoked = res["Concreteness"].beta
evoked.plot_image(mask=reject_H0, time_unit='s')