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.

Modules

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