Abstract
A multi voxel pattern analysis classification framework suitable for neuroimaging data is introduced. The framework includes a novel feature extraction method that uses local modeling based on domain specific knowledge, and therefore, can produce better whole-brain global classification performance using a smaller number of features. In particular, the method includes spherical searchlights in combination with local SVM modeling. The performance of the framework is demonstrated on a challenging fMRI classification problem, and is found to be superior to the performance of state-of-the-art feature selection methods used in neuroimaging.
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Jamshy, S., Perez, O., Yeshurun, Y., Hendler, T., Intrator, N. (2012). Searchlight Based Feature Extraction. In: Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. Lecture Notes in Computer Science(), vol 7263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34713-9_3
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DOI: https://doi.org/10.1007/978-3-642-34713-9_3
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