Feature transformation of neural activity with sparse and low-rank decomposition

We propose a novel application of the sparse and low-rank (SLR) decomposition method to decode cognitive states for concept activity measured using fMRI BOLD. Current decoding methods attempt to reduce the dimensionality of fMRI BOLD signals to increase classification rate, but do not address the separable issues of multiple noise sources and complexity in the underlying data. Our feature transformation method extends SLR to separate task activity from the resting state and extract concept specific cognitive state. We show a significant increase in single trial decoding of concepts from fMRI BOLD using SLR to extract task specific cognitive state.