A smart HMI for driving safety using emotion prediction of EEG signals

This paper provides an overview on the past pieces of literature on emotion prediction systems and the different machine learning algorithms used to classify emotions. We propose a system which incorporates the emotion prediction system with a custom Smart Human Machine Interface (SHMI) for vehicle drivers to improve drive safety. This is achieved based on EEG signals and basic vehicle information's obtained from an OBD (On-Board Diagnostics) data. EEG signals are classified into four emotional states: happy, sad, relaxed and angry. In this paper, we present an initial development of the Smart Human Machine Interface (SHMI) for emotion detection for vehicle applications. To evaluate the classification of the EEG signals we use Russell's circumflex model, Higuchi Fractal Dimension (HFD), PSD (Power Spectral Density) for feature extraction and Support Vector Machines (SVM) for classification.