Web: http://arxiv.org/abs/2205.03230

May 9, 2022, 1:11 a.m. | Zhe Liu, Yun Li, Lina Yao, Jessica J.M.Monaghan, David McAlpine

cs.LG updates on arXiv.org arxiv.org

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 model's poor generalization to new users, sessions or datasets.
Thus, designing a model that can generalize to new datasets is beneficial and
indispensable. To mitigate distribution discrepancy across datasets, we propose
to achieve Disentangled and Side-aware Unsupervised Domain Adaptation (DSUDA)
for cross-dataset tinnitus diagnosis. A …

arxiv cross dataset diagnosis domain adaptation unsupervised

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