Deep Feature Learning Using Target Priors with Applications in ECoG Signal Decoding for BCI / 1785
Zuoguan Wang, Siwei Lyu, Gerwin Schalk, Qiang Ji

Recent years have seen a great interest in using deep architectures for feature learning from data. One drawback of the commonly used unsupervised deep feature learning methods is that for supervised or semi-supervised learning tasks, the information in the target variables are not used until the final stage when the classifier or regressor is trained on the learned features. This could lead to over-generalized features that are not competitive on the specific supervised or semi-supervised learning tasks. In this work, we describe a new learning method that combines deep feature learning on mixed labeled and unlabeled data sets. Specifically, we describe a weakly supervised learning method of a prior supervised convolutional stacked auto-encoders (PCSA), of which information in the target variables is represented probabilistically using a Gaussian Bernoulli restricted Boltzmann machine (RBM). We apply this method to the decoding problem of an ECoG based Brain Computer Interface (BCI) system. Our experimental results show that PCSA achieves significant improvement in decoding performance on benchmark data sets compared to the unsupervised feature learning as well as to the current state-of-the-art algorithms that are based on manually crafted features.