EEG signals can non-invasively monitor the brain activities and have been widely used in brain-computer interfaces (BCI). One of the research areas is to recognize emotions objectively through EEG. In fact, the emotion of people changes over time, however, most of the existing affective BCIs process data and recognize emotions offline, and thus cannot be applied to real-time emotion recognition.
In order to solve this problem, we introduce the instance selection strategy into transfer learning and propose a simplified style transfer mapping algorithm. In the proposed method, the informative instances are firstly selected from the source domain data, and then the update strategy of hyperparameters is also simplified for style transfer mapping, making the model training more quickly and accurately for a new subject.
To verify the effectiveness of our algorithm, we carry out the experiments on SEED, SEED-IV and the offline dataset collected by ourselves, and achieve the recognition accuracies up to 86.78%, 82.55% and 77.68% in computing time of 7s, 4s and 10s, respectively. Furthermore, we also develop a real-time emotion recognition system which integrates the modules of EEG signal acquisition, data processing, emotion recognition and result visualization.
Both the results of offline and online experiments show that the proposed algorithm can accurately recognize emotions in a short time, meeting the needs of real-time emotion recognition applications.