|
| | | | |
| | | |
| Stat |
Members: 3645
Articles: 2'506'133
Articles rated: 2609
26 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
Supervised Hyperalignment for multi-subject fMRI data alignment | | Muhammad Yousefnezhad
; Alessandro Selvitella
; Liangxiu Han
; Daoqiang Zhang
; | | Date: |
9 Jan 2020 | | Abstract: | Hyperalignment has been widely employed in Multivariate Pattern (MVP)
analysis to discover the cognitive states in the human brains based on
multi-subject functional Magnetic Resonance Imaging (fMRI) datasets. Most of
the existing HA methods utilized unsupervised approaches, where they only
maximized the correlation between the voxels with the same position in the time
series. However, these unsupervised solutions may not be optimum for handling
the functional alignment in the supervised MVP problems. This paper proposes a
Supervised Hyperalignment (SHA) method to ensure better functional alignment
for MVP analysis, where the proposed method provides a supervised shared space
that can maximize the correlation among the stimuli belonging to the same
category and minimize the correlation between distinct categories of stimuli.
Further, SHA employs a generalized optimization solution, which generates the
shared space and calculates the mapped features in a single iteration, hence
with optimum time and space complexities for large datasets. Experiments on
multi-subject datasets demonstrate that SHA method achieves up to 19% better
performance for multi-class problems over the state-of-the-art HA algorithms. | | Source: | arXiv, 2001.2894 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER