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An overview of applications of ICA to biological data and general data mining, Computational Neurobiology Laboratory Salk Institute, La Jolla CA (April, 1999). Enter [Enter] to advance, [up-arrow] to rewind. Independent Component Analysis Perform “blind separation” of signals recorded at multiple sensors Use minimal assumptions about the characteristics of the signal sources. Principle: Maximize Information • Q: How to extract maximum information from multiple visual channels? • A: 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. Set of 144 ICA filters ICA versus PCA • Independent Principal Component Component Analysis (PCA) (ICA) finds finds directions of maximal independence variance in Gaussian in non-data Gaussian data statistics). (second-order (higher-order statistics). Example: Audio decomposition Perform ICA Mic 1 Mic 2 Mic 3 Mic 4 Terry Te-Won Play Mixtures Scott Tzyy-Ping Play Components Electroencephalography (EEG) Artifacts Brain signals • ICA separates brain signals from artifacts. • Permits study of brain activity in noisy conditions. • Allows monitoring of multiple brain processes. Functional Brain Imaging • Functional magnetic resonance imaging (fMRI) data are noisy and complex. ICA Component Types (b) (a) Sustained task-related (c) • ICA identifies concurrent hemodynamic processes. • Does not require a priori knowledge of time courses or spatial distributions. Transiently task-related (d) Quasi-periodic Slowly-varying (e) (f) Abrupt head movement Slow head movement Activated Suppressed Data Mining • ICA was applied to Armed Forces Vocation Aptitude Battery (ASVAB) test scores and Navy Fire Control School grades. • Two ICA components contributed to final school grade. • ICA may suggest more efficient and balanced selection criteria. This presentation by • Scott Makeig, Naval Health Research Center, San Diego • Tzyy-Ping Jung, Institute for Neural Computation, UCSD, La Jolla CA • Te-Won Lee, Salk Institute, La Jolla CA • Sigurd Enghoff, Salk Institute • Terrence J. Sejnowski, Salk Institute & UCSD