Classification of EEG-based Emotion for BCI Applications

Emotion plays an important role in human daily life and is a significant feature for interaction among people. Due to having adaptive role, it motivate human to respond stimuli in their environment quickly for improving their communication, learning and decision-making. With increasing role of brain computer interface (BCI) in interaction between users and computer, automatic emotion recognition has become an interesting area in the past decade. Emotion recognition could be carried out from the facial expression, gesture, speech and text, and could be record in several ways, like Electroencephalography (EEG), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), etc. In this work, feature extraction and classification of emotions have been evaluated on different methods to recognize and classify six emotional states such as fear, sad, frustrated, happy, pleasant and satisfied from inner emotion EEG signals. The results showed that using appropriate feature for extraction emotional state such as Discrete Wavelet Transform (DWT) and suitable learner such as Aftificial Neural Network (ANN), recognizer system can be accurately.