LUR: An Online Learning Model for EEG Emotion Recognition.

Emotion recognition based on EEG (Electroen-cephalogram) has been widely used in may scenarios, such as Brain-computer interface, medical health and entertainment, etc. However, large differences exist in subjects due to individual characteristics, and EEG data of different time periods usually distribute inconsistently, which hinder the further development of EEG-based research. In this paper, we propose a LUR model to partially address these challenges. In the first stage, we extracted candidate features based on neuroscience research and learned them using SVM or Naive Bayes algorithm. If the performance during the validation phase is satisfactory, we keep the features. Otherwise we replace the candidate features and use an online learning method named LUR(Learn, Unlearn, and Relearn), which continuously prunes the irrelevant connections of the current data and retains important connections to constructing a model more suitable for the subject. Because the proposed model is based on streaming data, it can continuously relearn and solve the problem that the input data is not i.i.d. to a certain extent. Experiments were conducted on two public datasets, DEAP and DREAMER, and competitive results were obtained.