|
Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Brain Decoding of Multiple Subjects for Estimating Visual Information Based on a Probabilistic Generative Model
| Title: | Brain Decoding of Multiple Subjects for Estimating Visual Information Based on a Probabilistic Generative Model |
| Authors: | Higashi, Takaaki Browse this author | | Maeda, Keisuke Browse this author | | Ogawa, Takahiro Browse this author →KAKEN DB | | Haseyama, Miki Browse this author →KAKEN DB |
| Keywords: | brain decoding | | functional magnetic resonance imaging (fMRI) | | multiple subjects | | visual features | | generative model |
| Issue Date: | 17-Aug-2022 |
| Publisher: | MDPI |
| Journal Title: | Sensors |
| Volume: | 22 |
| Issue: | 16 |
| Start Page: | 6148 |
| Publisher DOI: | 10.3390/s22166148 |
| 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. |
| Type: | article |
| URI: | http://hdl.handle.net/2115/86818 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
|
|
