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Found 5 papers, 1 papers with code
Subject-Independent Brain-Computer Interfaces with Open-Set Subject Recognition
In contrast, in this work, we introduce a style information encoder as an auxiliary task that classifies various source domains and recognizes open-set domains.
Calibration-Free Driver Drowsiness Classification based on Manifold-Level Augmentation
This paper proposes a calibration-free framework for driver drowsiness state classification using manifold-level augmentation.
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.
Subject-Independent Brain-Computer Interface for Decoding High-Level Visual Imagery Tasks
Communication between humans and a drone using electroencephalogram (EEG) signals is one of the most challenging issues in the BCI domain.
