Pattern classification - Brain Research Imaging Centre Edinburgh

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Advantages of pattern recognition Accounts for the spatial correlation of the data (multivariate aspect) • images are multivariate by nature. • can yield greater sensitivity than conventional (univariate) analysis. Enable <strong>classification</strong>/prediction of individual subjects • ‘Mind-reading’ or decoding applications • Clinical application Haynes & Rees, 2006
<strong>Pattern</strong> recognition framework Input (brain scans) X 1 X 2 X 3 No mathematical model available Machine Learning Methodology Output (control/patient) y 1 y 2 y 3 Computer-based procedures that learn a function from a series of examples Training Examples: (X 1 ,y 1 ),...,(X s ,y s ) Learning/Training Phase Generate a function or classifier f such that f Testing Phase Test Example X i f(x i ) y i f(X i ) = y i Prediction
Page 1 and 2: Pattern Recognition for Neuroimagin
Page 3 and 4: Overview • Introduction - Univari
Page 5 and 6: BOLD signal Univariate vs. multivar
Page 7: Neuroimaging data “feature vector
Page 11 and 12: voxel 2 Classification example L R
Page 13 and 14: Kernel approaches • Mathematical
Page 15 and 16: Linear classifier • hyperplanes t
Page 17 and 18: Support Vector Machine SVM = “max
Page 19 and 20: SVM vs. GP SVM Hard binary classif
Page 21 and 22: Training set Test set … … …
Page 23 and 24: Confusion matrix & accuracy Confusi
Page 25 and 26: Regression MSE • LOO error in one
Page 29 and 30: Example of masks Linear machine Wei
Page 33 and 34: Design Between subjects •2 groups
Page 35 and 36: Design: Within subject • Block or
Page 37 and 38: Design: Within subject • Block or
Page 39 and 40: Multiclass problem Multiclass machi
Page 41: Conclusions Key points: • More se
Page 44: Thank you for your attention! Any q

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