A survey of neural signal decoding based on domain adaptation

Abstract

An important objective in brain-computer interfaces (BCIs) is to develop robust and reliable neural signal decoders. However, the decoders will encounter challenges under cross-subject or cross-session conditions due to the randomness, non-stationarity, and individual variability of brain electrical activity. Reducing distributional differences is an exceptionally intuitive way to eliminate inter-subject/session differences and enhance decoder generalizability. In this context, domain adaptation (DA) emerges as a valuable technique, enabling the rapid transfer of knowledge acquired from large datasets with labeled data to new subjects or sessions. This paper provides a comprehensive survey of DA research in neural decoding from 2014 to the present. We categorize neural decoding methods related to DA by considering instance-based, feature-based, and model-based, which is motivated by three fundamental challenges in DA: How can one effectively select suitable source domains or samples for transfer? How can inter-domain distributional differences be minimized through feature space transformation? And how can decoder parameters be optimally shared? Additionally, several decoding methods that combine deep learning with DA are highlighted, given the significant advantages of deep learning over traditional feature extraction techniques. Furthermore, our paper explores the application of DA in complex scenarios, such as multiple source domains and low-resource settings. In summary, we have reviewed domain-adaptive decoding algorithms and their application considerations, while identifying various challenges that need to be addressed in future research.