Decoding

mne.decoding:

Decoding and encoding, including machine learning and receptive fields.

CSP([n_components, reg, log, cov_est, ...])	M/EEG signal decomposition using the Common Spatial Patterns (CSP).
EMS()	Transformer to compute event-matched spatial filters.
FilterEstimator(info, l_freq, h_freq[, ...])	Estimator to filter RtEpochs.
LinearModel([model])	Compute and store patterns from linear models.
PSDEstimator([sfreq, fmin, fmax, bandwidth, ...])	Compute power spectral density (PSD) using a multi-taper method.
Scaler([info, scalings, with_mean, with_std])	Standardize channel data.
TemporalFilter([l_freq, h_freq, sfreq, ...])	Estimator to filter data array along the last dimension.
TimeFrequency(freqs[, sfreq, method, ...])	Time frequency transformer.
UnsupervisedSpatialFilter(estimator[, average])	Use unsupervised spatial filtering across time and samples.
Vectorizer()	Transform n-dimensional array into 2D array of n_samples by n_features.
ReceptiveField(tmin, tmax, sfreq[, ...])	Fit a receptive field model.
TimeDelayingRidge(tmin, tmax, sfreq[, ...])	Ridge regression of data with time delays.
SlidingEstimator(base_estimator[, scoring, ...])	Search Light.
GeneralizingEstimator(base_estimator[, ...])	Generalization Light.
SPoC([n_components, reg, log, ...])	Implementation of the SPoC spatial filtering.
SSD(info, filt_params_signal, filt_params_noise)	M/EEG signal decomposition using the Spatio-Spectral Decomposition (SSD).

Functions that assist with decoding and model fitting:

compute_ems(epochs[, conditions, picks, ...])	Compute event-matched spatial filter on epochs.
cross_val_multiscore(estimator, X[, y, ...])	Evaluate a score by cross-validation.
get_coef(estimator[, attr, inverse_transform])	Retrieve the coefficients of an estimator ending with a Linear Model.