Decoding#
Decoding and encoding, including machine learning and receptive fields.
  | 
M/EEG signal decomposition using the Common Spatial Patterns (CSP).  | 
  | 
Transformer to compute event-matched spatial filters.  | 
  | 
Estimator to filter RtEpochs.  | 
  | 
Compute and store patterns from linear models.  | 
  | 
Compute power spectral density (PSD) using a multi-taper method.  | 
  | 
Standardize channel data.  | 
  | 
Estimator to filter data array along the last dimension.  | 
  | 
Time frequency transformer.  | 
  | 
Use unsupervised spatial filtering across time and samples.  | 
Transform n-dimensional array into 2D array of n_samples by n_features.  | 
|
  | 
Fit a receptive field model.  | 
  | 
Ridge regression of data with time delays.  | 
  | 
Search Light.  | 
  | 
Generalization Light.  | 
  | 
Implementation of the SPoC spatial filtering.  | 
  | 
Signal decomposition using the Spatio-Spectral Decomposition (SSD).  | 
  | 
Implementation of the Xdawn Algorithm compatible with scikit-learn.  | 
  | 
Container for spatial filter weights (evecs) and patterns.  | 
Functions that assist with decoding and model fitting:
  | 
Compute event-matched spatial filter on epochs.  | 
  | 
Evaluate a score by cross-validation.  | 
  | 
Retrieve the coefficients of an estimator ending with a Linear Model.  | 
  | 
Instantiate a   |