Unsupervised domain adaptation techniques based on auto-encoder for non-stationary EEG-based emotion recognition

In electroencephalography (EEG)-based emotion recognition systems, the distribution between the training samples and the testing samples may be mismatched if they are sampled from different experimental sessions or subjects because of user fatigue, different electrode placements, varying impedances, etc. Therefore, it is difficult to directly classify the EEG patterns with a conventional classifier. The domain adaptation method, which is aimed at obtaining a common representation across training and test domains, is an effective method for reducing the distribution discrepancy.

Source: Computers in Biology and Medicine - October 21, 2016 Category: Bioinformatics Authors: Xin Chai, Qisong Wang, Yongping Zhao, Xin Liu, Ou Bai, Yongqiang Li Source Type: research