Search Results for author:
Found 2 papers, 0 papers with code
Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces
For this purpose, in this paper, we proposed a framework that employs the open-set recognition technique as an auxiliary task to learn subject-specific style features from the source dataset while helping the shared feature extractor with mapping the features of the unseen target dataset as a new unseen domain.
Confidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface
The main purpose of this paper is to propose a method of excluding subjects that are expected to have a negative impact on subject-to-subject TL training, which generally uses data from as many subjects as possible.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
