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
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PCA finds the directions of maximum variance
ICA finds the directions of maximum
independence
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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.
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Example: Audio decomposition
Play Mixtures Play Components
Perform ICA
Mic 1
Mic 2
Mic 3
Mic 4
Terry Scott
Te-Won Tzyy-Ping
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ICA Applications
• Sound source separation • Image processing• Sonar target identification• Underwater communications• Wireless communications• Brain wave analysis (EEG) • Brain imaging (fMRI)
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Recordings in real environmentsSeparation of Music & Speech
Experiment-Setup:- office room (5m x 4m)- two distant talking mics- 16kHz sampling rate
40cm
60cm
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Learning Image Features
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Learning Image Features
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Automatic Image Segmentation
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Barcode Classification
Matrix Linear
Postal
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Learned ICA Output Filters
Matrix Postal Linear
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Barcode Classification Results
Classifying 4 data sets: linear, postal, matrix, junk
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Image De-noising
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Filling in missing data
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ICA applied to BrainwavesAn EEG recording consists of activity arising from many brain and extra-brain processes
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Eye movement
Muscle activity
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WHAT ARE THE INDEPENDENT
COMPONENTS OF BRAIN IMAGING?
Measured Signal
Task-related activations Arousal
Physiologic Pulsations
Machine Noise
?
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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.