ICA Projects at CNL

- ICA algorithms

- ICA applied to electrophysiological data analysis

- ICA applied to functional resonance imaging data analysis

- Face recognition using ICA

- Lip-reading using ICA

Te-Won Lee, Michael Lewicki, Tony Bell(at Interval Research) Terry Sejnowski

Scott Makeig, Tzyy-Ping Jung, Martin McKeown Colin Humphries, Terry Sejnowski

**Definitions of terms -- EEG,
MEG, ERP, ERF**

Electromagnetic fields associated with brain processes and recorded outside the head produce electroencephalographic (EEG) and magnetoencephalographic (MEG) data. Averages of EEG epochs time-locked to a set of experimental events of interest are called event-related potentials (ERPs). Similar magnetic averages are known as event-related fields or ERFs.

**Suitability for ICA decomposition**

In the EEG/MEG frequency range (roughly 0.1-100 Hz) the mixing of brain fields at the scalp electrodes is basically linear. Although skull attenuates EEG signals strongly and "smears" (low-pass filters) them spatially, this does not affect the linear relation between potential in the brain and potential at the scalp. Fields propagate to the sensors (electrodes or SQUID coils) through volume conduction without significant delays. This makes EEG and MEG data suited to linear decomposition via ICA. A number of "frequently asked questions" about the application of ICA to averaged or spontaneous EEG/MEG data are answered in Frequently Asked Questions about ICA applied to EEG/MEG data.

**First
Applications**

The ICA algorithm of Bell &
Sejnowski was first applied to EEG and ERP data in Makeig S, Bell AJ,
Jung T-P, and Sejnowski TJ, "Independent component analysis of
electroencephalographic data." *Advances in Neural Information
Processing Systems* 8, 145-151,1996. This paper demonstrated the
successful decomposition of 14-channel ERP data consisting of only 624
data points. Further details have now been published in a PNAS paper on
ICA applied to ERP data. Preprint html and
Postscript versions of this paper are also available for review
and download from this site.

**Toolbox**

A Matlab toolbox for EEG/MEG analysis using ICA is also available for download. The toolbox consists of scripts for ICA decomposition and plotting of results, together with general-purpose EEG plotting and computational routines. A demo script (icademo) illustrates application of the ICA routines to both synthetic and actual ERP data.

http://www.cnl.salk.edu/~scott/ica.html

View summary of recent changes to the toolbox.

Bibliography of publications on biomedical applications of ICAMartin McKeown, Tzyy-Ping Jung, Scott Makeig, Terry Sejnowski

fMRI data

fMRI data is a complicated mixture of different sources of variability: cardiac and respiratory pulsations, subtle head movements, task-related activity changes and machine noise. Changes related to the performance of psychomotor tasks may constitute as little as 10-15% of the variance of the Blood Oxygen Level Dependent (BOLD) contrast signal in a 1.5T magnet, so extracting the small task-related changes from the measured signal is difficult.

**ICA decomposition of fMRI data**

ICA, in the manner applied to ERP and EEG (see above), is
inappropriate for fMRI analysis because the number of "channels"
(i.e. voxels) greatly exceeds the number of time points in a typical
fMRI experiment. In 1997, it was first proposed to look for
*spatially independent* patterns of activity in fMRI data [ref]. This
assumes that the spatial distributions associated with each of the
above sources of variability are independent, and that the
contributions from each spatial pattern sum linearly to represent the
data. The time courses associated with the different spatial patterns
can potentially be correlated, allowing for the detection of spatial
patterns whose time courses are transiently task-related (TTR) as well
as consistently task-related (CTR). The criteria of spatial
independence appears to be a powerful way to separate task-related
activations from other sources of variability making up the BOLD
signal, as explained in Frequently Asked
Questions about ICA applied to fMRI data.

Marni Bartlett, Terry Sejnowski

In a task such as face recognition, much of the important information
may be contained in the high-order relationships among the image pixels.
Some success has been attained using data-driven face representations based
on principal component analysis, such as "Eigenfaces" (Turk & Pentland,
1991) and "Holons" (Cottrell & Metcalfe, 1991). Principal component
analysis (PCA) is based on the second-order statistics of the image set,
and does not address high-order statistical dependencies such as the
relationships among three or more pixels. Independent component analysis
(ICA) is a generalization of PCA which separates the high-order moments of
the input in addition to the second-order moments. We developed image
representations based on the independent components of the face images and
compared them to a PCA representation for face recognition.

ICA was performed on the face images under two different
architectures. The first architecture provided a set of statistically
independent basis images for the faces that can be viewed as a set of
independent facial features. These ICA basis images were spatially
local, unlike the PCA basis vectors. The representation consisted of the
coefficients for the linear combination of basis images that comprised each
face image. The second architecture produced independent coding variables
(coefficients). This provided a factorial face code, in which the
probability of any combination of features can be obtained from the product
of their individual probabilities. The distributions of these coefficents
were sparse and highly kurtotic. Classification was performed using nearest
neighbor, with similarity measured as the cosine of the angle between
representation vectors. Both ICA representations were superior to the PCA
representation for recognizing faces across sessions, changes in
expression, and changes in pose.

Papers
on face image analysis using ICA by Marian Stewart Bartlett.

What is the appropriate spatial scale for image representation? In the primate visual system, receptive fields are small at early stages of processing (area V1), and larger at late stages of processing (areas MT, IT). In the current work, we explore the efficiency of local and global image representations on an automatic visual speech recognition task using an HMM as the recognition system. We compare local and global principal component and independent component image representations for the task. Local representations consistently and significantly outperformed global representations in terms of generalization to new speakers.

Gray, M.S., Movellan, J.R., and Sejnowski, T. J. (1997). A comparison of local versus global image decompositions for visual speechreading. Proceedings of the 4th Annual Jount Symposium on Neural Computation, Pasadena, CA, May 17, 1997.