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Development of a Wireless Safety Helmet Mobile APP Using EEG Signal Analysis

A. S. Abdel-Rahman 1, A. F. Seddik 1, and D. M. Shawky 2
1. Biomedical Engineering Department, Faculty of Engineering, Helwan University, Cairo, Egypt
2. Engineering Mathematics Department, Faculty of Engineering, Cairo University, 12613 Giza, Egypt
Abstract—The recent technology in digital computing created many ways of drowsiness detection. This is important because of the increased numbers of accidents caused by drowsy drivers. In this paper, an approach for detecting drowsiness state by continuously analyzing EEG signals is proposed. Using a single dry-sensor EEG headset, a real-time system that monitors and analyzes the EEG signal of the driver is developed. It automatically produces an alarm to alert the driver via an Android mobile application in case of detecting stage-one sleep. In addition to being portable, the system reached an average accuracy of 97.6% with a low false positive rate in a sample of 60 subjects using the statistical characteristics of the EEG waves. 
Index Terms—drowsiness detection, EEG classification, neural networks, mobile app

Cite: A. S. Abdel-Rahman, A. F. Seddik, and D. M. Shawky, "Development of a Wireless Safety Helmet Mobile APP Using EEG Signal Analysis," International Journal of Signal Processing Systems, Vol. 4, No. 5, pp. 432-436, October 2016. doi: 10.18178/ijsps.4.5.432-436
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