Adaptive Adversarial Transfer Learning for Electroencephalography Classification

Insufficient training data is a serious problem in all domains related to bioinformatics. Large-scale annotated electroencephalography (EEG) datasets are almost impossible to acquire because biological data acquisition is challenging and quality annotation is costly. Transfer learning relaxes the hypothesis that the training data must be independent and identically distributed (i.i. d.) with the test data, which motivates us to use transfer learning to solve the problem of insufficient training data in bioinformatics. We propose a new approach to transfer knowledge via a deep transfer learning framework, which includes an adaptive sample selection algorithm and a joint adversarial training algorithm. The adaptive sample selection algorithm dynamically adjusts the sample weights during the training process according to the distance between the source domain and the target domain. The joint adversarial training algorithm forces the network to learn a feature extractor suitable for the target domain based on a dataset from the source domain by using an adversarial network and a specific loss function. The experiments demonstrate that our approach has many advantages, such as robustness and accuracy, when applied to EEG classification tasks.