EEG-Based Emotion Recognition with Prototype-Based Data Representation

Emotions play an important role in human communication, and EEG signals are widely used for emotion recognition. Despite the extensive research of EEG in recent year, it is still challenging to interpret EEG signals effectively due to the massive noises in EEG signals. In this paper, we propose an effective emotion recognition framework, which contains two main parts: the representation network and the prototype selection algorithm. Through our proposed representation network, samples from the same kind of emotion state are more close to each other in high-level representation, and then, we selected the prototypes from the clustering set in feature space match the following testing samples. This method takes advantage of the powerful representation ability of deep learning and learns a better describable feature space rather than learn the classifier explicitly. The experiments on SEED dataset achieves a high accuracy of 93.29% and outperforms a set of baseline methods and the recent deep learning emotion classification approaches. These experimental results demonstrate the effectiveness of our proposed emotion recognition framework.