Disentangled and Side-Aware Unsupervised Domain Adaptation for Cross-Dataset Subjective Tinnitus Diagnosis
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Biomedical and Health Informatics"
DOI: 10.1109/jbhi.2022.3225089
Abstract: EEG-based tinnitus classification is a valuable tool for tinnitus diagnosis, research, and treatments. Most current works are limited to a single dataset where data patterns are similar. But EEG signals are highly non-stationary, resulting in… read more here.
Keywords: tinnitus diagnosis; unsupervised domain; domain; side aware ... See more keywords
Side-Aware Meta-Learning for Cross-Dataset Listener Diagnosis With Subjective Tinnitus
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Neural Systems and Rehabilitation Engineering"
DOI: 10.1109/tnsre.2022.3201158
Abstract: With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses. Although machine learning has been widely applied in EEG-based tinnitus analysis, most current models are dataset-specific.… read more here.
Keywords: meta learning; side aware; cross dataset; dataset ... See more keywords