SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG. (arXiv:2206.01323v1 [cs.LG])

Electroencephalography (EEG) provides access to neuronal dynamics
non-invasively with millisecond resolution, rendering it a viable method in
neuroscience and healthcare. However, its utility is limited as current EEG
technology does not generalize well across domains (i.e., sessions and
subjects) without expensive supervised re-calibration. Contemporary methods
cast this transfer learning (TL) problem as a multi-source/-target unsupervised
domain adaptation (UDA) problem and address it with deep learning or shallow,
Riemannian geometry aware alignment methods. Both directions have, so far,
failed to consistently …

arxiv domain adaptation normalization unsupervised