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Recently, visual encoding based on functional magnetic resonance imaging(fMRI) have realized many achievements with the rapid development of deepnetwork computation. Visual encoding model is aimed at predicting brainactivity in response to presented image stimuli. Currently, visual encoding isaccomplished mainly by firstly extracting image features through convolutionalneural network (CNN) model pre-trained on computer vision task, and secondlytraining a linear regression model to map specific layer of CNN features toeach voxel, namely voxel-wise encoding. However, the two-step manner model,essentially, is hard to determine which kind of well features are well linearlymatched for beforehand unknown fMRI data with little understanding of humanvisual representation. Analogizing computer vision mostly related human vision,we proposed the end-to-end convolution regression model (ETECRM) in the regionof interest (ROI)-wise manner to accomplish effective and efficient visualencoding. The end-to-end manner was introduced to make the model automaticallylearn better matching features to improve encoding performance. The ROI-wisemanner was used to improve the encoding efficiency for many voxels. Inaddition, we designed the selective optimization including self-adapting weightlearning and weighted correlation loss, noise regularization to avoidinterfering of ineffective voxels in ROI-wise encoding. Experiment demonstratedthat the proposed model obtained better predicting accuracy than the two-stepmanner of encoding models. Comparative analysis implied that end-to-end mannerand large volume of fMRI data may drive the future development of visualencoding.

Effective and efficient ROI-wise visual encoding using an end-to-end CNN regression model and selective optimization