Multi-Voxel Pattern Classification
Multi-voxel pattern classification (MVPC) is gaining increasing interest in real-time fMRI data analysis (e.g. LaConte et al., 2007; Sorger et al., 2010) and in the neuroimaging community in general because it allows to detect differences between conditions with higher sensitivity than conventional univariate analysis by focusing on the analysis and comparison of distributed patterns of activity. In such a multivariate approach, data from many voxels are jointly analyzed. The high sensitivity of MVPC allows "brain reading" applications that aim to decode ("predict") specific mental states or representational content from fMRI activity patterns. After performing a "training" or "learning" phase, this prediction requires little computational load and it is, thus, suitable for real-time fMRI including brain-computer interface (BCI) applications.
Version 3.0 of Turbo-BrainVoyager introduced multi-voxel pattern classification based on the widely used support vector machine (SVM) learning algorithm. Training and testing (prediction) is controlled in two separate steps. In the Analysis menu, the SVM Training item can be used to open the Multi-Voxel Pattern Classification dialog allowing to train a SVM on the data from one or more completed runs of a real-time session; the dialog can also be used to perform offline testing, e.g. on the data of a completed run. For trial-by-trial or moment-to-moment real-time classification, the Real-Time SVM Classification dialog can be used that can be invoked by clicking the Real-Time SVM Classification item in the Analysis menu. After training runs are completed, this dialog allows to analyze trials of a new run incrementally producing prediction values that predicts (ideally with high accuracy) to which class a distributed activity pattern belongs.
LaConte, S. M., Peltier, S. J., & Hu, X. P. (2007). Real-time fMRI using brain-state classification. Human Brain Mapping, 28, 1033-1044.
Sorger, B., Peters, J., van den Boomen, C., Zilverstand, A., Reithler, J. & Goebel, R. (2010). Real-time decoding of the locus of visuospatial attention using multi-voxel pattern classification. Proceedings of the Human Brain Mapping Conference.
Turbo-BrainVoyager uses LIBSVM for training SVM classifiers:
Chih-Chung Chang and Chih-Jen Lin, LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
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