Brain-in-Car: A Brain Activity-based Emotion Recognition Embedded System for Automotive

• Published in: IEEE International Conference on Vehicular Electronics and Safety proceedings (Online) pp. 1 - 5
• Main Authors: El-Amin, Abdelrahman, Eldawlatly, Seif, Attia, Ahmed, Hammad, Omar, Nasr, Osama, Ghozlan, Osama, Raouf, Remon, Hamed, Ahmed M., Eldawlatly, Hany, El-Moursy, Magdy
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2019
• Subjects: AUTOSAR; Electroencephalography; Machine Learning
• ISSN: 2643-9751
• DOI: 10.1109/ICVES.2019.8906392

Emotional distress during driving can greatly affect the safety and comfort of the driver. Being able to detect and react to the emotions of the driver would greatly improve in-car safety. It could also be utilized in a variety of different applications to improve the driving experience. In this paper, we introduce a brain signal-operated emotion recognition system that is specifically tailored for the Automotive Open System Architecture (AUTOSAR) framework. The proposed system acquires brain electroencephalography (EEG) signals of the driver, identifies the underlying emotion using machine learning techniques, and feeds that emotion into the car system where different car components can react to that input. Our results demonstrate the ability of the system to recognize two emotions, namely sadness versus happiness, from the recorded EEG with a mean accuracy of 89.7% across three examined subjects using subject-dependent data. Moreover, when training the system using data recorded from multiple subjects, a mean accuracy of 91.7% is achieved. Taken together, these results indicate the ability of the proposed approach to discriminate between sadness and happiness whose extreme expression could have a significant impact on driving behavior.