Subject-Independent Brain-Computer Interfaces with Open-Set Subject Recognition

A brain-computer interface (BCI) can't be effectively used since electroencephalography (EEG) varies between and within subjects. BCI systems require calibration steps to adjust the model to subject-specific data. It is widely acknowledged that this is a major obstacle to the development of BCI...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Han, Dong-Kyun, Kim, Dong-Young, Jang, Geun-Deok
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 19.01.2023
Subjects
Coders
Computer Science - Artificial Intelligence
Domains
Electroencephalography
Feature extraction
Human-computer interface
Recognition
Online AccessGet full text

Cover

More Information
Summary:A brain-computer interface (BCI) can't be effectively used since electroencephalography (EEG) varies between and within subjects. BCI systems require calibration steps to adjust the model to subject-specific data. It is widely acknowledged that this is a major obstacle to the development of BCIs. To address this issue, previous studies have trained a generalized model by removing the subjects' information. 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. Open-set recognition method was used as an auxiliary task to learn subject-related style information from the source subjects, while at the same time helping the shared feature extractor map features in an unseen target. This paper compares various OSR methods within an open-set subject recognition (OSSR) framework. As a result of our experiments, we found that the OSSR auxiliary network that encodes domain information improves generalization performance.
ISSN:2331-8422
DOI:10.48550/arxiv.2301.07894