Online Kernel SVM for real-time fMRI brain state prediction

The Support Vector Machine (SVM) methodology is an effective, supervised, machine learning method that gives stateof-the-art performance for brain state classification from functional magnetic resonance brain images (fMRI). Due to the poor scalability of SVM (cubic in the number of training points) and the massive size of fMRI images, a SVM analysis is usually performed after data collection. Recent advances in real-time fMRI applications, such as Brain Computer Interfaces, require a fast and reliable classification method running in synchronization with the image collection. We design an online Kernel SVM (OKSVM) algorithm based on the Sequential Minimization Optimization (SMO) method, that is fast (training on each new image within 1 sec), has memory and time cost that scales linearly with the number of points used, and yields comparable prediction performance to an offline SVM. We analyze the method’s performance by testing it on real fMRI data sets, and show that OKSVM perform...

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Yongxin Taylor Xi, Hao Xu, Ray Lee, Peter J. Ramadge
• Keywords: FMRI, ICASSP 2011, Online Kernel Svm, Real-time FMRI, Signal Processing