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Driving Control Using Emotion Analysis Via EEG
Aditya Bhide, , Anuja Kulkarni, Chinmayi Bankar, Chirag Ghube
Published in IEEE
Driving is a very taxing job as the visual, sensory and motor inputs work in harmony to make driving a successful experience. However, when the harmony is distorted, as in the cases when the driver is frustrated, anxious, the negative mindset can be dangerous as it can lead to potential accidents. Analyzing the emotions of the driver and striving to control them therefore becomes important. The work in this paper focuses on enhancing the current driving system of cars to help reduce the accident rate. It uses analysis of driver emotions via the data obtained from EEG (Electroencephalography) waves and the valence-Arousal models for emotion classification. Additionally, the control of emotions is obtained via the concept of music therapy. The proposed approach uses the music system in the car to automatically respond to changes in the emotional state of the driver. The emotion detection would be relatively real time thus efficiently soothing the driver. Moreover, the automatic adjustment of music in consonance with the mood would reduce the manual interfacing of the knobs in the car, thus saving distraction and reducing the chances of accidents. © 2018 IEEE.
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JournalData powered by Typeset2018 IEEE Punecon
PublisherData powered by TypesetIEEE
Open AccessNo