Online kernel SVM for real-time fMRI brain state prediction

Yongxin Taylor Xi, Hao Xu, Ray Lee, Peter Jeffrey Ramadge

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages2040-2043
Number of pages4
DOIs
StatePublished - Aug 18 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: May 22 2011May 27 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
CountryCzech Republic
CityPrague
Period5/22/115/27/11

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Online kernel SVM for real-time fMRI brain state prediction'. Together they form a unique fingerprint.

  • Cite this

    Xi, Y. T., Xu, H., Lee, R., & Ramadge, P. J. (2011). Online kernel SVM for real-time fMRI brain state prediction. In 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings (pp. 2040-2043). [5946913] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2011.5946913