| RUNICA | Perform Independent Component Analysis (ICA) decomposition of psychophysiological data using the infomax ica algorithm of Bell & Sejnowski (1995) with the natural gradient feature of Amari, Cichocki & Yang, the extended-ICA algorithm of Lee, Girolami & Sejnowski, PCA dimension reduction, and/or specgram() preprocessing (suggested by M. Zibulevsky). |
| Usage: | >> [weights,sphere] = runica(data); >> [weights,sphere,activations,bias,signs,lrates] = runica(data,'Key1',Value1',...); |
| Input: | |
data |
input data (chans,frames*epochs). Note that if data consists of multiple discontinuous epochs, each epoch should be separately baseline-zero'd using >> data = rmbase(data,frames,basevector); |
| Optional keywords: | |
'ncomps' |
[N] number of ica components to compute (default -> chans) using rectangular ica decomposition |
'pca' |
[N] decompose a principal component (default -> 0=off) subspace of the data. Value is the number of PCs to retain. |
'sphering' |
['on'/'off'] flag sphering of data (default -> 'on') |
'weights' |
[W] initial weight matrix (default -> eye()) (Note: if 'sphering' 'off', default -> spher) |
'lrate' |
[rate] initial ica learning rate (<< 1) (default -> heuristic) |
'block' |
[N] ica block size (<< datalength) (default -> heuristic) |
'anneal' |
annealing constant (0,1] (defaults -> 0.90, or 0.98, extended) controls speed of convergence |
'annealdeg' |
[N] degrees weight change for annealing (default -> 70) |
'stop' |
[f] stop training when weight-change < this (default -> 1e-6) |
'maxsteps' |
[N] max number of ica training steps (default -> 512) |
'bias' |
['on'/'off'] perform bias adjustment (default -> 'on') |
'momentum' |
[0 |
'extended' |
[N] perform tanh() "extended-ICA" with sign estimation every N training blocks. If N < 0, fix number of sub-Gaussian components to -N [faster than N>0] (default|0 -> off) |
'specgram' |
[srate loHz hiHz frames winframes] decompose a complex time/frequency transform of the data (Note: winframes must divide frames) (defaults [srate 0 srate/2 size(data,2) size(data,2)]) |
'posact' |
make all component activations net-positive(default 'on'} |
'verbose' |
give ascii messages ('on'/'off') (default -> 'on') |
| Outputs: | [RO: output in reverse order of projected mean variance unless starting weight matrix passed ('weights' above)] |
weights |
ica weight matrix (comps,chans) [RO] |
sphere |
data sphering matrix (chans,chans) = spher(data) Note that unmixing_matrix = weights*sphere {sphering off -> eye(chans)} |
activations |
activation time courses of the output components (ncomps,frames*epochs) |
bias |
vector of final (ncomps) online bias [RO] (default = zeros()) |
signs |
extended-ICA signs for components [RO] (default = ones()) [ -1 = sub-Gaussian; 1 = super-Gaussian] |
lrates |
vector of learning rates used at each training step |
| Authors: | Scott Makeig with contributions from Tony Bell, Te-Won Lee, Tzyy-Ping Jung, Sigurd Enghoff, Michael Zibulevsky, CNL/The Salk Institute, La Jolla, 1996- |