Note

Click here to download the full example code

Contents

Decoding (a.k.a. MVPA) in MNE largely follows the machine
learning API of the scikit-learn package.
Each estimator implements `fit`

, `transform`

, `fit_transform`

, and
(optionally) `inverse_transform`

methods. For more details on this design,
visit scikit-learn. For additional theoretical insights into the decoding
framework in MNE, see [1].

For ease of comprehension, we will denote instantiations of the class using the same name as the class but in small caps instead of camel cases.

Let’s start by loading data for a simple two-class problem:

```
# sphinx_gallery_thumbnail_number = 6
import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
import mne
from mne.datasets import sample
from mne.decoding import (SlidingEstimator, GeneralizingEstimator, Scaler,
cross_val_multiscore, LinearModel, get_coef,
Vectorizer, CSP)
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
tmin, tmax = -0.200, 0.500
event_id = {'Auditory/Left': 1, 'Visual/Left': 3} # just use two
raw = mne.io.read_raw_fif(raw_fname, preload=True)
# The subsequent decoding analyses only capture evoked responses, so we can
# low-pass the MEG data. Usually a value more like 40 Hz would be used,
# but here low-pass at 20 so we can more heavily decimate, and allow
# the examlpe to run faster. The 2 Hz high-pass helps improve CSP.
raw.filter(2, 20)
events = mne.find_events(raw, 'STI 014')
# Set up pick list: EEG + MEG - bad channels (modify to your needs)
raw.info['bads'] += ['MEG 2443', 'EEG 053'] # bads + 2 more
picks = mne.pick_types(raw.info, meg='grad', eeg=False, stim=True, eog=True,
exclude='bads')
# Read epochs
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks, baseline=(None, 0.), preload=True,
reject=dict(grad=4000e-13, eog=150e-6), decim=10)
epochs.pick_types(meg=True, exclude='bads') # remove stim and EOG
X = epochs.get_data() # MEG signals: n_epochs, n_meg_channels, n_times
y = epochs.events[:, 2] # target: Audio left or right
```

Out:

```
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
Read a total of 3 projection items:
PCA-v1 (1 x 102) idle
PCA-v2 (1 x 102) idle
PCA-v3 (1 x 102) idle
Range : 25800 ... 192599 = 42.956 ... 320.670 secs
Ready.
Current compensation grade : 0
Reading 0 ... 166799 = 0.000 ... 277.714 secs...
Setting up band-pass filter from 2 - 20 Hz
l_trans_bandwidth chosen to be 2.0 Hz
h_trans_bandwidth chosen to be 5.0 Hz
Filter length of 991 samples (1.650 sec) selected
320 events found
Event IDs: [ 1 2 3 4 5 32]
145 matching events found
Applying baseline correction (mode: mean)
Not setting metadata
3 projection items activated
Loading data for 145 events and 421 original time points ...
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
22 bad epochs dropped
```

The `mne.decoding.Scaler`

will standardize the data based on channel
scales. In the simplest modes `scalings=None`

or `scalings=dict(...)`

,
each data channel type (e.g., mag, grad, eeg) is treated separately and
scaled by a constant. This is the approach used by e.g.,
`mne.compute_covariance()`

to standardize channel scales.

If `scalings='mean'`

or `scalings='median'`

, each channel is scaled using
empirical measures. Each channel is scaled independently by the mean and
standand deviation, or median and interquartile range, respectively, across
all epochs and time points during `fit`

(during training). The `transform()`

method is
called to transform data (training or test set) by scaling all time points
and epochs on a channel-by-channel basis. To perform both the `fit`

and
`transform`

operations in a single call, the
`fit_transform()`

method may be used. To invert the
transform, `inverse_transform()`

can be used. For
`scalings='median'`

, scikit-learn version 0.17+ is required.

Note

Using this class is different from directly applying
`sklearn.preprocessing.StandardScaler`

or
`sklearn.preprocessing.RobustScaler`

offered by
scikit-learn. These scale each *classification feature*, e.g.
each time point for each channel, with mean and standard
deviation computed across epochs, whereas
`mne.decoding.Scaler`

scales each *channel* using mean and
standard deviation computed across all of its time points
and epochs.

Scikit-learn API provides functionality to chain transformers and estimators
by using `sklearn.pipeline.Pipeline`

. We can construct decoding
pipelines and perform cross-validation and grid-search. However scikit-learn
transformers and estimators generally expect 2D data
(n_samples * n_features), whereas MNE transformers typically output data
with a higher dimensionality
(e.g. n_samples * n_channels * n_frequencies * n_times). A Vectorizer
therefore needs to be applied between the MNE and the scikit-learn steps
like:

```
# Uses all MEG sensors and time points as separate classification
# features, so the resulting filters used are spatio-temporal
clf = make_pipeline(Scaler(epochs.info),
Vectorizer(),
LogisticRegression(solver='lbfgs'))
scores = cross_val_multiscore(clf, X, y, cv=5, n_jobs=1)
# Mean scores across cross-validation splits
score = np.mean(scores, axis=0)
print('Spatio-temporal: %0.1f%%' % (100 * score,))
```

Out:

```
Spatio-temporal: 99.2%
```

The `mne.decoding.PSDEstimator`

computes the power spectral density (PSD) using the multitaper
method. It takes a 3D array as input, converts it into 2D and computes the
PSD.

The `mne.decoding.FilterEstimator`

filters the 3D epochs data.

Just like temporal filters, spatial filters provide weights to modify the data along the sensor dimension. They are popular in the BCI community because of their simplicity and ability to distinguish spatially-separated neural activity.

`mne.decoding.CSP`

is a technique to analyze multichannel data based
on recordings from two classes [2] (see also
http://en.wikipedia.org/wiki/Common_spatial_pattern).

Let \(X \in R^{C\times T}\) be a segment of data with \(C\) channels and \(T\) time points. The data at a single time point is denoted by \(x(t)\) such that \(X=[x(t), x(t+1), ..., x(t+T-1)]\). Common spatial pattern (CSP) finds a decomposition that projects the signal in the original sensor space to CSP space using the following transformation:

(1)¶\[x_{CSP}(t) = W^{T}x(t)\]

where each column of \(W \in R^{C\times C}\) is a spatial filter and each row of \(x_{CSP}\) is a CSP component. The matrix \(W\) is also called the de-mixing matrix in other contexts. Let \(\Sigma^{+} \in R^{C\times C}\) and \(\Sigma^{-} \in R^{C\times C}\) be the estimates of the covariance matrices of the two conditions. CSP analysis is given by the simultaneous diagonalization of the two covariance matrices

(2)¶\[W^{T}\Sigma^{+}W = \lambda^{+}\]

(3)¶\[W^{T}\Sigma^{-}W = \lambda^{-}\]

where \(\lambda^{C}\) is a diagonal matrix whose entries are the eigenvalues of the following generalized eigenvalue problem

(4)¶\[\Sigma^{+}w = \lambda \Sigma^{-}w\]

Large entries in the diagonal matrix corresponds to a spatial filter which gives high variance in one class but low variance in the other. Thus, the filter facilitates discrimination between the two classes.

Examples

Note

The winning entry of the Grasp-and-lift EEG competition in Kaggle used
the `CSP`

implementation in MNE and was featured as
a script of the week.

We can use CSP with these data with:

```
csp = CSP(n_components=3, norm_trace=False)
clf = make_pipeline(csp, LogisticRegression(solver='lbfgs'))
scores = cross_val_multiscore(clf, X, y, cv=5, n_jobs=1)
print('CSP: %0.1f%%' % (100 * scores.mean(),))
```

Out:

```
Estimating covariance using EMPIRICAL
Done.
Estimating covariance using EMPIRICAL
Done.
Estimating covariance using EMPIRICAL
Done.
Estimating covariance using EMPIRICAL
Done.
Estimating covariance using EMPIRICAL
Done.
Estimating covariance using EMPIRICAL
Done.
Estimating covariance using EMPIRICAL
Done.
Estimating covariance using EMPIRICAL
Done.
Estimating covariance using EMPIRICAL
Done.
Estimating covariance using EMPIRICAL
Done.
CSP: 86.4%
```

Source Power Comodulation (`mne.decoding.SPoC`

) [3]
identifies the composition of
orthogonal spatial filters that maximally correlate with a continuous target.

SPoC can be seen as an extension of the CSP where the target is driven by a continuous variable rather than a discrete variable. Typical applications include extraction of motor patterns using EMG power or audio patterns using sound envelope.

Examples

`mne.preprocessing.Xdawn`

is a spatial filtering method designed to
improve the signal to signal + noise ratio (SSNR) of the ERP responses [4].
Xdawn was originally
designed for P300 evoked potential by enhancing the target response with
respect to the non-target response. The implementation in MNE-Python is a
generalization to any type of ERP.

The result of `mne.decoding.EMS`

is a spatial filter at each time
point and a corresponding time course [5].
Intuitively, the result gives the similarity between the filter at
each time point and the data vector (sensors) at that time point.

When interpreting the components of the CSP (or spatial filters in general), it is often more intuitive to think about how \(x(t)\) is composed of the different CSP components \(x_{CSP}(t)\). In other words, we can rewrite Equation (1) as follows:

(5)¶\[x(t) = (W^{-1})^{T}x_{CSP}(t)\]

The columns of the matrix \((W^{-1})^T\) are called spatial patterns. This is also called the mixing matrix. The example Linear classifier on sensor data with plot patterns and filters discusses the difference between patterns and filters.

These can be plotted with:

```
# Fit CSP on full data and plot
csp.fit(X, y)
csp.plot_patterns(epochs.info)
csp.plot_filters(epochs.info, scalings=1e-9)
```

Out:

```
Estimating covariance using EMPIRICAL
Done.
Estimating covariance using EMPIRICAL
Done.
```

This strategy consists in fitting a multivariate predictive model on each
time instant and evaluating its performance at the same instant on new
epochs. The `mne.decoding.SlidingEstimator`

will take as input a
pair of features \(X\) and targets \(y\), where \(X\) has
more than 2 dimensions. For decoding over time the data \(X\)
is the epochs data of shape n_epochs x n_channels x n_times. As the
last dimension of \(X\) is the time, an estimator will be fit
on every time instant.

This approach is analogous to SlidingEstimator-based approaches in fMRI, where here we are interested in when one can discriminate experimental conditions and therefore figure out when the effect of interest happens.

When working with linear models as estimators, this approach boils down to estimating a discriminative spatial filter for each time instant.

We’ll use a Logistic Regression for a binary classification as machine learning model.

```
# We will train the classifier on all left visual vs auditory trials on MEG
clf = make_pipeline(StandardScaler(), LogisticRegression(solver='lbfgs'))
time_decod = SlidingEstimator(clf, n_jobs=1, scoring='roc_auc', verbose=True)
scores = cross_val_multiscore(time_decod, X, y, cv=5, n_jobs=1)
# Mean scores across cross-validation splits
scores = np.mean(scores, axis=0)
# Plot
fig, ax = plt.subplots()
ax.plot(epochs.times, scores, label='score')
ax.axhline(.5, color='k', linestyle='--', label='chance')
ax.set_xlabel('Times')
ax.set_ylabel('AUC') # Area Under the Curve
ax.legend()
ax.axvline(.0, color='k', linestyle='-')
ax.set_title('Sensor space decoding')
```

You can retrieve the spatial filters and spatial patterns if you explicitly use a LinearModel

```
clf = make_pipeline(StandardScaler(),
LinearModel(LogisticRegression(solver='lbfgs')))
time_decod = SlidingEstimator(clf, n_jobs=1, scoring='roc_auc', verbose=True)
time_decod.fit(X, y)
coef = get_coef(time_decod, 'patterns_', inverse_transform=True)
evoked = mne.EvokedArray(coef, epochs.info, tmin=epochs.times[0])
joint_kwargs = dict(ts_args=dict(time_unit='s'),
topomap_args=dict(time_unit='s'))
evoked.plot_joint(times=np.arange(0., .500, .100), title='patterns',
**joint_kwargs)
```

Temporal generalization is an extension of the decoding over time approach. It consists in evaluating whether the model estimated at a particular time instant accurately predicts any other time instant. It is analogous to transferring a trained model to a distinct learning problem, where the problems correspond to decoding the patterns of brain activity recorded at distinct time instants.

The object to for Temporal generalization is
`mne.decoding.GeneralizingEstimator`

. It expects as input \(X\)
and \(y\) (similarly to `SlidingEstimator`

) but
generates predictions from each model for all time instants. The class
`GeneralizingEstimator`

is generic and will treat the
last dimension as the one to be used for generalization testing. For
convenience, here, we refer to it as different tasks. If \(X\)
corresponds to epochs data then the last dimension is time.

This runs the analysis used in [6] and further detailed in [7]:

```
# define the Temporal generalization object
time_gen = GeneralizingEstimator(clf, n_jobs=1, scoring='roc_auc',
verbose=True)
scores = cross_val_multiscore(time_gen, X, y, cv=5, n_jobs=1)
# Mean scores across cross-validation splits
scores = np.mean(scores, axis=0)
# Plot the diagonal (it's exactly the same as the time-by-time decoding above)
fig, ax = plt.subplots()
ax.plot(epochs.times, np.diag(scores), label='score')
ax.axhline(.5, color='k', linestyle='--', label='chance')
ax.set_xlabel('Times')
ax.set_ylabel('AUC')
ax.legend()
ax.axvline(.0, color='k', linestyle='-')
ax.set_title('Decoding MEG sensors over time')
```

Plot the full (generalization) matrix:

```
fig, ax = plt.subplots(1, 1)
im = ax.imshow(scores, interpolation='lanczos', origin='lower', cmap='RdBu_r',
extent=epochs.times[[0, -1, 0, -1]], vmin=0., vmax=1.)
ax.set_xlabel('Testing Time (s)')
ax.set_ylabel('Training Time (s)')
ax.set_title('Temporal generalization')
ax.axvline(0, color='k')
ax.axhline(0, color='k')
plt.colorbar(im, ax=ax)
```

Source space decoding is also possible, but because the number of features can be much larger than in the sensor space, univariate feature selection using ANOVA f-test (or some other metric) can be done to reduce the feature dimension. Interpreting decoding results might be easier in source space as compared to sensor space.

Examples

- Explore other datasets from MNE (e.g. Face dataset from SPM to predict Face vs. Scrambled)

[1] | Jean-Rémi King et al. (2018) “Encoding and Decoding Neuronal Dynamics: Methodological Framework to Uncover the Algorithms of Cognition”, in press. https://hal.archives-ouvertes.fr/hal-01848442/ |

[2] | Zoltan J. Koles. The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG. Electroencephalography and Clinical Neurophysiology, 79(6):440–447, December 1991. |

[3] | Dahne, S., Meinecke, F. C., Haufe, S., Hohne, J., Tangermann, M., Muller, K. R., & Nikulin, V. V. (2014). SPoC: a novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters. NeuroImage, 86, 111-122. |

[4] | Rivet, B., Souloumiac, A., Attina, V., & Gibert, G. (2009). xDAWN algorithm to enhance evoked potentials: application to brain-computer interface. Biomedical Engineering, IEEE Transactions on, 56(8), 2035-2043. |

[5] | Aaron Schurger, Sebastien Marti, and Stanislas Dehaene, “Reducing multi-sensor data to a single time course that reveals experimental effects”, BMC Neuroscience 2013, 14:122 |

[6] | Jean-Remi King, Alexandre Gramfort, Aaron Schurger, Lionel Naccache and Stanislas Dehaene, “Two distinct dynamic modes subtend the detection of unexpected sounds”, PLOS ONE, 2013, http://www.ncbi.nlm.nih.gov/pubmed/24475052 |

[7] | King & Dehaene (2014) ‘Characterizing the dynamics of mental representations: the temporal generalization method’, Trends In Cognitive Sciences, 18(4), 203-210. http://www.ncbi.nlm.nih.gov/pubmed/24593982 |

**Total running time of the script:** ( 0 minutes 24.174 seconds)

**Estimated memory usage:** 489 MB