Feature extraction of emotional states for EEG-based rage control

We conduct an emotion recognition study to understand and classify the emotional states of human since the social problems caused by impulsive rage explosions such as the revenge driving, noise floor, and violent crimes. Recently, the emotion recognition technology using electroencephalogram (EEG) has become the foremost consideration of researchers compared with others using voice, facial image, bio-signal, and etc. However, the recognition and assumption of an emotion condition based on direct EEG face a challenge due to its characteristics, namely noise, non-stationary, and non-linear. Therefore, a further appropriate algorithm for feature extraction is more desirable to classify the emotion state from EEG data. In this paper, a multiclass-common spatial patterns (Multi-CSP) is proposed to extract the features with respect to each emotional state. Then, support vector machine (SVM) model is set up to classify these features of emotional state for the rage control. The experimental results show that the proposed feature extraction method, Multi-CSP, performs higher classification accuracy than the popular feature extraction methods.