REAL-TIME CONJUGATE GRADIENTS FOR ONLINE FMRI CLASSIFICATION
Presented by: Hao Xu, Author(s): Hao Xu, Yongxin Xi, Ray Lee, Peter Ramadge, Princeton University, United States
Real-time functional magnetic resonance imaging (rtfMRI) enables classification of brain activity during data collection. The advantage over batch fMRI data processing is that inference results are accessible as feedback to both the subject and the experimenter as data collection proceeds. The major challenge of rtfMRI is the potential loss of accuracy in inference that the resource limitations of 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 offline analysis method. We give a theoretical comparison with PLS and show that the algorithm generates identical results to PLS given appropriate initial conditions. However, we show that in practice there is a speed advantage to using an alternative initial condition. Experimental results show that the online rtCG classifier: is fast (training time < 0.5s), is accurate (prediction accuracy approximately 90%), can adapt to a varying stimulus, and yields better classification performance than standard PLS applied to a sliding window of recent data.
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