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Functional Brain Signal Processing: EEG & fMRI Lesson 16

M.Tech. (CS), Semester III, Course B50. Functional Brain Signal Processing: EEG & fMRI Lesson 16. Kaushik Majumdar Indian Statistical Institute Bangalore Center kmajumdar@isibang.ac.in. Poldrack et al., 2011. Multi-Voxel Pattern Analysis.

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Functional Brain Signal Processing: EEG & fMRI Lesson 16

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  1. M.Tech. (CS), Semester III, Course B50 Functional Brain Signal Processing: EEG & fMRILesson 16 Kaushik Majumdar Indian Statistical Institute Bangalore Center kmajumdar@isibang.ac.in

  2. Poldrack et al., 2011 Multi-Voxel Pattern Analysis MVPA is concerned about simultaneous activation patterns across multiple voxels in different parts of the brain with the help of machine learning algorithms.

  3. Haynes & Rees, 2006 Haxby et al., 2001 A Task Specific MVPA FFA = fusiform face area (red). PPA = parahippocampal place area. During face image presentation (red arrow) signal is enhanced in FFA and during building image presentation (blue arrow) signal is enhanced in PPA. Activation patterns in temporal lobe during visualization of chair and shoe. r is correlation coefficient between activation patterns on same and different objects.

  4. Norman et al., 2006 A Hypothetical Scheme for MVPA Computation A cortical activation pattern coding scheme during chair and shoe visualization. Two dimensional projection of high dimensional feature space where binary classification has been accomplished by the red-dashed line.

  5. Haynes & Rees, 2006 MVPA by Statistical Pattern Recognition

  6. Four Basic Steps of MVPA • Feature selection – which voxels will be involved in classification analysis. • Pattern assembly – sorting the data into discrete brain patterns corresponding to the patterns of activity across selected voxels at a particular time in the experiment.

  7. Basic Steps (cont) • Classifier training – feeding a subset of the leveled patterns into a multivariate pattern classification algorithm. The algorithm learns a function that maps a voxel activity pattern into an experimental condition. • Generalization testing – putting the classification algorithm to test on hitherto un-presented data.

  8. Nature of Classifiers • Most MVPA studies used linear classifiers including correlation based classifiers. • Neural networks. • Support vector machine. • Bayesian classifiers.

  9. Haxby et al., 2001 Correlation Based Classifier

  10. References • K. A. Norman, S. M. Polyn, G. E. Detre and J. V. Haxby, Beyond mind-reading: multi-voxel pattern analysis of fMRI data, Trends Cog. Sc., 10(9): 424 – 430, 2006. • R. A. Poldrack, J. A. Mumford and T. E. Nichols, Handbook of Functional MRI Data Analysis, Cambridge University Press, Cambridge, New York, 2011. Chapter 10.

  11. References (cont) • J.-D. Haynes and G. Rees, Decoding mental states from brain activity in humans, Nat. Rev. Neurosci., 7: 523 – 534, 2006. • J. V. Haxby et al., Distributed and overlapping representations of faces and objects in ventral temporal cortex, Science, 293: 2425 – 2430, 2001.

  12. THANK YOUThis lecture is available at http://www.isibang.ac.in/~kaushik

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