| Paper ID | BIO-4.4 |
| Paper Title |
SUBSPACE ODDITY - OPTIMIZATION ON PRODUCT OF STIEFEL MANIFOLDS FOR EEG DATA |
| Authors |
Maria Sayu Yamamoto, Tokyo University of Agriculture and Technology, Japan; Maria Sayu Yamamoto, Université Paris-Saclay, France; Florian Yger, Université Paris-Dauphine, France; Sylvain Chevallier, Université Paris-Saclay, France |
| Session | BIO-4: Machine Learning and Signal Processing for Neural Signals |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Biomedical Imaging and Signal Processing: [BIO-BCI] Brain/human-computer interfaces |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Dimensionality reduction of high-dimensional electroencephalography (EEG) covariance matrices is crucial for effective utilization of Riemannian geometry in Brain-Computer Interfaces (BCI). In this paper, we propose a novel similarity-based classification method that relies on dimensionality reduction of EEG covariance matrices. Conventionally, the dimension of the original high-dimensional space is reduced by projecting into one low-dimensional space, and the similarity is learned only based on the single space. In contrast, our method, MUltiple SUbspace Mdm Estimation (MUSUME), obtains multiple low-dimensional spaces that enhance class separability by solving the proposed optimization problem, then the similarity is learned in each low-dimensional space. This multiple projection approach encourages finding the space that is more useful for similarity learning. Experimental evaluation with high-dimensionality EEG datasets (128 channels) confirmed that MUSUME proved significant improvement for classification (p < 0.001) and also it showed the potential to beat the existing method relying on only one subspace representation. |
