Target image search using fMRI signals

Recent neural signal decoding studies based on functional magnetic resonance imaging (fMRI) have identified the specific image presenting to the subject from a set of potential images, and some studies extend neural decoding into image reconstruction, i.e. image contents that the subject perceived were decoded from the fMRI signals recorded during the subject looking at images. In this paper, we decoded the target images using fMRI signals and described a target image searching method based on the relationship between target image stimuli and fMRI activity. We recorded fMRI data during a serial visual stimuli image presentation task, some of the stimuli images were target images and the rest images were non-target ones. Our fMRI data analysis results showed that in the serial visual presentation task, target images elicited a stereotypical response in the fMRI, which can be detected by multi-voxel pattern analysis (MVPA). Classifiers designed with support vector machine (SVM) used this response to decipher target images from non-target images. The leave-one-run-out cross-validation showed that we can pick out the target images with a possibility far above the chance level, which indicate that there’s a neural signatures correlated with the target image recognition process in the human systems.