Model-Based Machine Learning for Real-Time Brain Decoding: Neurofeedback derived from real-time functional magnetic resonance imaging (rtfMRI) is promising for both scientific applications, such as uncovering hidden brain networks that respond to stimulus, and clinical applications, such as helping people cope with brain disorders ranging from addiction to autism. One of the greatest challenges in applying machine learning to real time brain “decoding” is that traditional methods fit per-voxel parameters, leading to large computational problems on relatively small datasets. As such, it is easy to over-fit parameters to noise rather than the desired signals. Bayesian model-based hierarchical topographical factor analysis (HTFA) solves this problem by uncovering low-dimensional representations (latent factors) of brain images, fitting parameters for latent factors (rather than voxels) while removing the false assumption that all voxels are independent. In this talk, we’ll discuss the promise of using this and other model-based machine learning to better understand full-brain activity and functional connectivity. And we’ll show how Intel Labs and its partners are combining neuroscience and computer science expertise to further extend such algorithms for real-time brain decoding.