spatial_filtering Package

spatial_filtering Package

Erase and/or recombine channels of multivariate TimeSeries

Spatial Filtering subsumes methods, that can be used to extract the most relevant information contained in a number of channels and create a number of pseudo channels, that are (linear) combinations of the former channels.

Typically, spatial filtering requires a training phase in which a number of training examples is presented and a model is created that is later on used to concentrate the relevant information contained in the signal in a small number of pseudo channels. There are both unsupervised and supervised spatial filtering methods.


channel_difference Build the difference of channels based on different criteria
channel_selection Select a subset of concrete specified channels
csp Original and variants of the Common Spatial Pattern algorithm
fda Fisher’s Discriminant Analysis and variants for spatial filtering
ica Independent Component Analysis variants
pca Principal Component Analysis variants
rereferencing Change the reference of an EEG signal
sensor_selection Methods for sensor selection optimization algorithms
spatial_filtering Basic methods for spatial filtering
xdawn xDAWN and variants for enhancing event-related potentials