Brain Decoding of Multiple Subjects for Estimating Visual Information Based on a Probabilistic Generative Model

Abstract

Brain decoding is a process of decoding human cognitive contents from brain activities. However, improving the accuracy of brain decoding remains difficult due to the unique characteristics of the brain, such as the small sample size and high dimensionality of brain activities. Therefore, this paper proposes a method that effectively uses multi-subject brain activities to improve brain decoding accuracy. Specifically, we distinguish between the shared information common to multi-subject brain activities and the individual information based on each subject’s brain activities, and both types of information are used to decode human visual cognition. Both types of information are extracted as features belonging to a latent space using a probabilistic generative model. In the experiment, an publicly available dataset and five subjects were used, and the estimation accuracy was validated on the basis of a confidence score ranging from 0 to 1, and a large value indicates superiority. The proposed method achieved a confidence score of 0.867 for the best subject and an average of 0.813 for the five subjects, which was the best compared to other methods. The experimental results show that the proposed method can accurately decode visual cognition compared with other existing methods in which the shared information is not distinguished from the individual information.

Trial Analysis of the Relationship between Taste and Biological Information Obtained While Eating Strawberries for Sensory Evaluation

“…Additionally, several studies estimated the interests of content involving human behavior [ 19 , 20 ] and personalized saliency (and its prediction) using gaze data [ 21 , 22 , 23 ]. Furthermore, the analysis of behavior, gaze data, and brain activity contribute to the solutions to several tasks, such as brain decoding [ 24 , 25 , 26 , 27 ] and certain applications [ 28 , 29 , 30 , 31 , 32 ]. Onuma et al [ 31 , 32 ] showed that human brain activity calculated from functional near-infrared spectroscopy (fNIRS) is related to the taste of food [ 31 ].…”