Negative-Sample-Free Contrastive Self-Supervised Learning for Electroencephalogram-Based Motor Imagery Classification

Negative-Sample-Free Contrastive Self-Supervised Learning for Electroencephalogram-Based Motor Imagery Classification

Motor imagery-based brain-computer interface (MI-BCI) systems convert user intentions into computer commands, aiding the communication and rehabilitation of individuals with motor disabilities. Traditional MI classification relies on supervised learning; however, it faces challenges in acquiring lar...

Полное описание

Библиографические подробности
Главные авторы: In-Nea Wang, Choel-Hui Lee, Hakseung Kim, Dong-Joo Kim
Формат: Статья
Язык:English
Опубликовано: IEEE 2024-01-01
Серии:IEEE Access
Предметы:
Brain-computer interface
contrastive learning
electroencephalography
motor imagery
self-supervised learning
Online-ссылка:https://ieeexplore.ieee.org/document/10680037/

Internet

https://ieeexplore.ieee.org/document/10680037/

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