Estimating human emotion evoked by visual stimuli using fMRI data
Kento Sugata, Takahiro Ogawa, Miki Haseyama · 2016
This paper presents a method that estimates human emotion evoked by visual stimuli using functional magnetic resonance imaging (fMRI) data. First, in our method, preprocessing and masking procedures are applied to the fMRI data. These procedures provide the multiple brain data corresponding to Brodmann areas (BA). In most cases, the dimensionality of fMRI data and the BA data is larger than the number of observations, and this results in overfitting. Thus, in order to reduce the dimensionality, we apply general tensor discriminant analysis (GTDA), which can take into account the information related to the users' emotion. Then multiple estimation results of the users' emotion are obtained from support vector machine by separately using the multiple BA data obtained after the dimensionality reduction via GTDA. Furthermore, our method obtains the final estimation result from effective supervised decision-level fusion of the above estimation results.