Learning to Transfer Knowledge from Multiple Sources of Electrophysiological Signals

Abstract

Deep learning methods have shown unparalleled performance when trained on vast amounts of diverse labeled training data, often collected at great cost. In many contexts, we have lots of labeled examples but only a few individuals, can be thought of as “little big data,” where we would like to take advantage of the large number of samples while still being cognizant of the fact that the number of observed groups is small. This problem is often known as domain adaptation or transfer learning. In this dissertation, I will cover four major topics. Electroencephalography (EEG) and Local Field Potential (LFP) signals are “big” in terms of the size of recorded data but rarely have sufficient labels required to train complex models. Furthermore, they are collected from limited number of individuals. The first topic I will introduce an interpretable neuro model for electrophysiological signals and explain why transfer learning helps in real situations.In the following two topics, I will expand the discussion of transfer learning problem with two real and challenging setups. Since data outliers will always exist in practice, many of the sources may be irrelevant to the target task, so ignoring the structure of the dataset is detrimental. Learning domain relationships are often insightful in their own right, and they allow domains to share strength without interference from irrelevant data. On top of the problems of outliers, label shift, where the percentage of data in each class is different between domains, is also essential for transfer learning.Transfer learning needs target sample during the training stage, while this requirement may not be satisfied in practice. The last topic discusses the situation on generalizing a trained model on unseen testing samples, where each training domain has a unique classifier and each test data point is predicted by an admixture over the different domain classifiers.