  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 disentangled auto-encoder is developed
to decouple class-irrelevant information from the EEG signals to improve the
classifying ability. The side-aware unsupervised domain adaptation module
adapts the class-irrelevant information as domain variance to a new dataset and
excludes the variance to obtain the class-distill features for the new dataset
classification. It also align signals of left and right ears to overcome
inherent EEG pattern difference. We compare DSUDA with state-of-the-art
methods, and our model achieves significant improvements over competitors
regarding comprehensive evaluation criteria. The results demonstrate our model
can successfully generalize to a new dataset and effectively diagnose tinnitus.
