The Support Vector Machine (SVM) methodology is an effective, supervised, machine learning method that gives state-of-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 off-line SVM.We analyze the method's performance by testing it on real fMRI data sets, and show that OKSVM performs well at greatly reduced computational cost. Our work provides a feasible online Kernel SVM for real-time fMRI experiments, and can be used to guide for the design of similar online classifiers in fMRI cognitive state classification.