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

3. PCA finds the directions of maximum variance ICA finds the directions of maximum independence

4. 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.

5. Example: Audio decomposition Play MixturesPlay Components Perform ICA Mic 1 Mic 2 Mic 3 Mic 4 Terry Scott Te-WonTzyy-Ping

6. ICA Applications Sound source separation Image processing Sonar target identification Underwater communications Wireless communications Brain wave analysis (EEG) Brain imaging (fMRI)

7. Recordings in real environments Separation of Music & Speech Experiment-Setup: - office room (5m x 4m) - two distant talking mics - 16kHz sampling rate 40cm 60cm

8. Learning Image Features

10. Automatic Image Segmentation

11. Barcode Classification MatrixLinear Postal

12. Learned ICA Output Filters Matrix Postal Linear

13. Barcode Classification Results Classifying 4 data sets: linear, postal, matrix, junk

14. Image De-noising

15. Filling in missing data

16. ICA applied to Brainwaves An EEG recording consists of activity arising from many brain and extra-brain processes

17. Eye movement Muscle activity

22. WHAT ARE THE INDEPENDENT COMPONENTS OF BRAIN IMAGING? Measured Signal Task-related activations Arousal Physiologic Pulsations Machine Noise ?

23. 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.

24. ICA-2001: http://www.ica2001.org Contact: terry@salk.edu