applications of independent component analysis terrence sejnowski computational neurobiology...
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Applications of Independent Component Analysis
Terrence Sejnowski
Computational Neurobiology LaboratoryComputational Neurobiology LaboratoryThe Salk InstituteThe Salk Institute
PCA finds the directions of maximum variance
ICA finds the directions of maximum
independence
Principle: Maximize Information
• Q:Q: How to extract maximum
information from multiple visual
channels?
Set of 144 ICA filters
• AA: ICA does this -- it maximizes
joint entropy & minimizes
mutual information between output
channels (Bell & Sejnowski, 1995).• ICA produces brain-like visual filters
for natural images.
Example: Audio decomposition
Play Mixtures Play Components
Perform ICA
Mic 1
Mic 2
Mic 3
Mic 4
Terry Scott
Te-Won Tzyy-Ping
ICA Applications
• Sound source separation • Image processing• Sonar target identification• Underwater communications• Wireless communications• Brain wave analysis (EEG) • Brain imaging (fMRI)
Recordings in real environmentsSeparation of Music & Speech
Experiment-Setup:- office room (5m x 4m)- two distant talking mics- 16kHz sampling rate
40cm
60cm
Learning Image Features
Learning Image Features
Automatic Image Segmentation
Barcode Classification
Matrix Linear
Postal
Learned ICA Output Filters
Matrix Postal Linear
Barcode Classification Results
Classifying 4 data sets: linear, postal, matrix, junk
Image De-noising
Filling in missing data
ICA applied to BrainwavesAn EEG recording consists of activity arising from many brain and extra-brain processes
Eye movement
Muscle activity
WHAT ARE THE INDEPENDENT
COMPONENTS OF BRAIN IMAGING?
Measured Signal
Task-related activations Arousal
Physiologic Pulsations
Machine Noise
?
Functional Brain Imaging
• Functional magnetic
resonance imaging (fMRI)
data are noisy and
complex.
I C A C o m p o n e n t T y p e s
S u s t a i n e d t a s k - r e l a t e d
( a )
T r a n s i e n t l yt a s k - r e l a t e d
( b )
S l o w l y - v a r y i n g
( c )
Q u a s i - p e r i o d i c
( d )
A b r u p t h e a dm o v e m e n t
( e )
A c t i v a t e dS u p p r e s s e d
S l o w h e a dm o v e m e n t
( f )
• ICA identifies concurrent
hemodynamic processes.
• Does not require a priori
knowledge of time courses
or spatial distributions.
