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A smart HMI for driving safety using emotion prediction of EEG signals
conference contributionposted on 2017-02-06, 00:00 authored by Gokul Thirunavukkarasu, Hamid AbdiHamid Abdi, Navid MohajerNavid Mohajer
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.
EventIEEE Systems, Man, and Cybernetics Society. Conference (2016 : Budapest, Hungary)
SeriesIEEE Systems, Man, and Cybernetics Society Conference
Pagination4148 - 4153
PublisherInstitute of Electrical and Electronics Engineers
Place of publicationPiscataway, N.J.
Publication classificationE Conference publication; E1 Full written paper - refereed
Copyright notice2016, IEEE
Title of proceedingsSMC 2016 : Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics
CategoriesNo categories selected
Electroencephalogram (EEG) SensorsSmart Human Machine Interface (SHMI)Support Vector Machines (SVM)PSD (Power Spectral Density)OBD (On-Board Diagnostics)Higuchi Fractal Dimension (HFD)Science & TechnologyTechnologyComputer Science, CyberneticsComputer Science, Information SystemsComputer ScienceRECOGNITIONCLASSIFICATIONMACHINE