A novel semi-supervised deep learning framework for affective state recognition on EEG signals

Nowadays the rapid development in the area of human-computer interaction has given birth to a growing interest on detecting different affective states through smart devices. By using the modern sensor equipment, we can easily collect electroencephalogram (EEG) signals, which capture the information from central nervous system and are closely related with our brain activities. Through the training on EEG signals, we can make reasonable analysis on people's affection, which is very promising in various areas. Unfortunately, the special properties of EEG dataset have brought difficulties for conventional machine learning methods. The main reasons lie in two aspects: the small set of labeled samples and the noisy channel problem. To overcome these difficulties and successfully identify the affective states, we come up with a novel semi-supervised deep structured framework. Compared with previous deep learning models, our method is more adapted to the EEG classification problem. We first adopt a two-level procedure, which involves both supervised label information and unsupervised structure information to jointly make decision on channel selection. And then, we add a generative Restricted Boltzmann Machine (RBM) model for the classification task, and use the training objectives of generative learning and unsupervised learning to jointly regularize the discriminative training. Finally, we extend it to the active learning scenario, which solves the costly labeling problem. The experiments conducted on real EEG dataset have shown both the convincing result on critical channel selection and the superiority of our method over multiple baselines for the affective state recognition.