Selection of Significant Brain Regions Based on MvGTDA and TS-DLF for Emotion Estimation

In this paper, we propose a novel method for estimating human emotion using functional brain images. The final goal of our study is contribution to affective brain computer interfaces (aBCIs), which use neuropsychological signals. In the proposed method, we newly derive multiview general tensor discriminant analysis (MvGTDA) in order to reveal significant brain regions and accurately estimate human emotion evoked by visual stimuli. This is because it is important to find activation of multiple brain regions for estimating emotional states. Since we regard a “Brodmann area”as a “view”and introduce L
₁
-norm regularization for these views, MvGTDA can eliminate non-crucial Brodmann areas and select significant ones. Moreover, in general studies on functional brain images based on machine learning methodologies, there is an overfitting problem caused by a small sample size. Therefore, revealing significant Brodmann areas based on MvGTDA has another important role, i.e., solving the overfitting problem. By inputting estimation results respectively obtained from the significant areas and the MvGTDA-based feature, tensor-based supervised decision-level fusion (TS-DLF) integrates them and outputs the final estimation result of the user's emotion. In experiments, we showed the effectiveness of our method by using actual functional brain images and we revealed the significant brain regions in emotional states.