Decoding cognitive states from fMRI data using Support Vector Regression

In this paper we describe a method based on Support Vector machines for Regression (SVR) to decode cognitive states from functional Magnetic Resonance Imaging (fMRI) data. In the context of the Pittsburgh Brain Activity Interpretation Competition (PBAIC, 2007), three participants were scanned during three runs of 20-minute immersion in a Virtual Reality Environment (VRE) where they played a game that engaged them in various search tasks. A set of objective feature ratings was automatically extracted from the VRE during the scanning session, whereas a set of subjective features was then derived from each individual experience. The aim of the present study was to explore the feasibility of the SVR approach in the case of an extremely complex regression problem, in which subjective experience of participants immersed in a VRE had to be predicted from their fMRI data. The proposed methodology was modeled as a multiphase process: a pre-processing phase, based on a filter approach, for fMRI image voxel selection, and a prediction phase, implemented by nonlinear SVR, for decoding subjective cognitive states from the selected voxel time series. Results highlight the generalization ability of nonlinear SVR, making this approach particularly interesting for real world application of Brain Computer Interface (BCI).