F-score feature selection based Bayesian reconstruction of visual image from human brain activity

Decoding perceptual experience from human brain activity is a big challenge in neuroscience. Recent advances in human neuroimaging have shown that it is possible to reconstruct a person's visual experience based on the retinotopy in the early visual cortex and the multivariate pattern analysis (MVPA) method using functional magnetic resonance imaging (fMRI). Previous researches reconstructed binary contrast-defined images using combination of multi-scale local image decoders in V1, V2 and V3, where contrast for local image bases was predicted from fMRI activity by sparse multinomial logistic regression (SMLR) and other models. However, the precision and efficiency of the visual image reconstruction remain insufficient. Proper feature selection is widely known to be as critical for prediction and reconstruction. Aiming at the shortcomings of existing reconstruction models, we proposed a new model of Bayesian reconstruction based on F-score feature selection (Bayes+F). The results indicate that the proposed Bayes+F model has better reconstruction accuracy and higher efficiency than the SMLR and other models, showing better robustness and noise resistant ability. It can improve the spatial correlation coefficient (Mean  ±  variance: 0.7078  ±  0.2104) and decrease the standard error (Mean  ±  variance: 0.2693  ±  0.0871) between the stimulus and the reconstructed image. Furthermore, the proposed model can reconstruct the images extremely rapid, 100 times faster than SMLR does.