Applications of Independent Component Analysis Terrence Sejnowski Computational Neurobiology Laboratory The Salk Institute.

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Presentation transcript:

Applications of Independent Component Analysis Terrence Sejnowski Computational Neurobiology Laboratory The 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 MixturesPlay Components Perform ICA Mic 1 Mic 2 Mic 3 Mic 4 Terry Scott Te-WonTzyy-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 environments Separation of Music & Speech Experiment-Setup: - office room (5m x 4m) - two distant talking mics - 16kHz sampling rate 40cm 60cm

Learning Image Features

Automatic Image Segmentation

Barcode Classification MatrixLinear 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 Brainwaves An 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. ICA identifies concurrent hemodynamic processes. Does not require a priori knowledge of time courses or spatial distributions.

ICA-2001: Contact: