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binica.sc
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# ica - Perform infomax independent component analysis
# the binary ica application.
#
# Master input settings file for running the ICA algorithm of Bell & Sejnowski
# (1996) and/or the extended-ICA algorithm of Lee, Girolami & Sejnowski (1998).
# via the stand-alone C++-coded binary. This is the master .sc file.
# Normally, do not alter this file. Matlab function binica() makes
# a customized copy in the pwd and runs the binary ica application from
# that copy. Original Matlab code (runica.m) by Scott Makeig with
# code contributions by Tony Bell, Te-Won Lee, et al. C++ translation
# by Sigurd Enghoff, CNL / Salk Institute 7/98
#
# The MATLAB binica() routine can be used to call binary ica from Matlab.
# Usage: >> [wts,sph] = binica(data,[runica() args]);
#
# Contacts: {scott,jung}@sccn.ucsd.edu
# {tony,terry}@salk.edu
#
# Required input variables:
#
DataFile XXX # Input data to decompose (native floats)
# multiplexed by channel (i.e., c1, c2, ...))
chans 31 # Number of data channels (= data columns)
frames 768 # Number of data points (= data rows)
#
# Output variables:
#
WeightsOutFile binica.wts # Output ICA weight matrix (floats)
SphereFile binica.sph # Output sphering matrix (floats)
#
# Note: input data files must be, and output files will be native floats.
#
# Processing options:
#
sphering on # Flag sphering of data (on/off) {default: on}
bias on # Perform bias adjustment (on/off) {default: on}
extended 0 # Perform "extended-ICA" using tnah() with kurtosis
# estimation every N training blocks. If N < 0,
# fix number of sub-Gaussian components to -N
# {default|0: off}
pca 0 # Decompose a principal component subspace of
# # the data. Retain this many PCs. {default|0: all}
# Optional input variables:
#
lrate 1.0e-4 # Initial ICA learning rate (float << 1)
# {default: heuristic ~5e-4}
blocksize 0 # ICA block size (integer << datalength)
# {default: heuristic fraction of log data length}
stop 1.0e-7 # Stop training when weight-change < this value
# {default: heuristic ~0.0000001}
maxsteps 512 # Max. number of ICA training steps {default: 128}
posact off # Make each component activation net-positive
# (on/off) NB: requires extra space! {default: off}
annealstep 0.98 # Annealing factor (range (0,1]) - controls
# the speed of convergence.
annealdeg 60 # Angledelta threshold for annealing {default: 60}
momentum 0 # Momentum gain (range [0,1]) {default: 0}
verbose on # Give ascii messages (on/off) {default: on}
#
# Optional input starting weights:
#
# WeightsInFile file # Starting ICA weight matrix (nchans,ncomps)
# # {default: identity or sphering matrix}
#
# Optional output files:
#
# ActivationsFile data.act # Activations of each component (ncomps,points)
# BiasFile data.bs # Bias weights (ncomps,1)
# SignFile data.sgn # Signs designating (-1) sub- and (1) super-Gaussian
# components (ncomps,1) from 'extended' mode training
# Unused flags
#
# epochs 436 # Number of epochs
# FrameWindow 20 # Number of frames per window
# FrameStep 4 # Number of frames to step per window
# EpochWindow 100 # Number of epochs per window
# EpochStep 25 # Number of epochs to step per window
# Baseline 25 # Number of data points contained in baseline