Real-time conjugate gradients for online fMRI classification

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

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

1 Scopus citations

Abstract

Real-time functional magnetic resonance imaging (rtfMRI) enables classification of brain activity during data collection thus making inference results accessible to both the subject and experimenter during the experiment. The major challenge of rtfMRI is the potential loss of inference accuracy due to the resource limitations that rtfMRI imposes. For example, many widely-used analysis methods in off-line neuroimaging are too time-consuming for rtfMRI. We develop an online, real-time, conjugate gradient (rtCG) algorithm that learns to classify brain states as data is being collected. The algorithm is closely connected to partial least squares (PLS), a popular off-line analysis method. We give a theoretical comparison with PLS and show that the algorithm generates identical results to PLS for appropriate initial conditions. However, in practice using an alternative initial condition yields faster convergence. Experimental results show that the online rtCG classifier: is fast (training time < 0.5s), is accurate (prediction accuracy ≈ 90%), can adapt to a varying stimulus, and yields better classification performance than standard PLS applied to a sliding window of recent data.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages565-568
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

Keywords

  • Conjugate Gradient
  • Online learning
  • Partial Least Squares
  • fMRI classification

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  • Cite this

    Xu, H., Xi, Y. T., Lee, R., & Ramadge, P. J. (2011). Real-time conjugate gradients for online fMRI classification. In 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings (pp. 565-568). [5946466] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2011.5946466