The physiological signal detection approach is to measure the changes of driver or operator biological signals, such as the electroencephalography (EEG) and electrocardiogram (ECG). Since the sleep rhythm is strongly correlated with brain and heart activities (brain rhythms correlated with NREM first stage), these physiological signals can give more accurate drowsiness detection than video monitoring. The drawback of this method is that the electrode contacts and wires can make a discomfort for the driver or operator and also that the electrodes must be placed on the skull using a conductive gel which ensures a good contact with the skin. These difficulties can be overcome by using dry-contact, low-noise EEG sensors, as it is described in [3]. The dry-contact EEG electrodes were created by the authors [3] with micro-electrical-mechanical system (MEMS) technology. Each channel of the analog signal processing front-end comes on a custom-built, small-sized circuit board which contains an amplifier, filters, and analog-to-digital conversion. As the authors describe daisy-chain configuration between boards with bit-serial output reduces the wiring needed and the system is capable to detect alpha-band rhythms and eye-blink signals.

Also for driver awareness detection a number of methods were proposed which are based on the technique of embedding biosensors into steering wheel or in the seat of the vehicle in order to measure heart beat pulse signals [4]. Time series of heart beat pulse signal can be used to calculate the heart rate variability. As it is shown in [4], the frequency domain spectral analysis of heart rate variability shows that typical it has three main frequency bands: high frequency band, low frequency band and very low frequency. A number of psycho-physiological researches have found that the low and high frequency power spectral density ratio decreases when a person changes from waking into drowsiness/sleep stage, while the high frequency power increases associated with this status change. The authors in [4] show that this variability can be an effective method for the detection of driver drowsiness. The problem with this method is that the drowsiness detection equipment is not portable and it must be embedded in the vehicle parts which will limit its applicability.

Analyzing the above mentioned approaches we decided to study algorithms which can be used for drowsiness/awareness detection when applying techniques based on EEG and EMG signals measurement. There are also a large number of studies which uses EEG signals for brain – computer interface applications [5, 6, 7, 8]. If the EEG device is built in the manner described in [3] this method has the advantage that is independent from the equipment, vehicle or situ where it is used, and can be applied both for drivers and equipment operators too.

References:

1. Carlson, N. R., Sleep and Biological Rhythms. in Physiology of Behavior, 7th ed. Boston: Allyn & Bacon Publishers, 2001.
2. Sleep Research Society, Basics of Sleep Behavior Syllabus, WebSciences International and Sleep Research Society, 1997.
3. Sullivan, T. J., S. R. Deiss, T.-P. Jung, G. Cauwenberghs, A Brain-Machine Interface using Dry-Contact, Low-Noise EEG Sensors, 2008 IEEE International Symposium on Circuits and Systems 18-21 May 2008, Washington, US
4. X. Yu, Real-time Non-intrusive Detection of Driver Drowsiness, Intelligent Transportation Systems Institute Center for Transportation Studies, University of Minnesota, May 2009.
5. Daly, J. J., J. R. Wolpaw, Brain-Computer Interfaces in Neurological Rehabilitation, Volume 7, Issue 11, November 2008, Pages 1032-1043
6. Ting, J.-A., A. D’Souza, K. Yamamoto, T. Yoshioka, D. Hoffman, S. Kakeif, L. Sergio, J. Kalaska, M. Kawato, P. Strick, S. Schaal, Variational Bayesian Least Squares: An Application to Brain-machine Interface Data, Neural Networks, Volume 21, Issue 8, October 2008, Pages 1112-1131.
7. Cvetkovic, D., E. D. Übeyli, I. Cosic, Wavelet Transform Feature Extraction from Human PPG, ECG, and EEG Signal Responses to ELF PEMF Exposures: A Pilot Study, Digital Signal Processing, Volume 18, Issue 5, September 2008, Pages 861-874.
8. Ting, W., Y. Guo-zheng, Y. Bang-hua, S. Hong, EEG Feature Extraction Based on Wavelet Packet Decomposition for Brain Computer Interface, Measurement, Volume 41, Issue 6, July 2008, Pages 618-62.
9. Georgescu, V., Fuzzy Time Series Estimation and Prediction: Criticism, Suitable New Methods and Experimental Evidence, Studies in Informatics and control, Volume 19 , Issue 3, 2010.
10. Abid, H., M. Chtourou, A. Toumi, Robust Fuzzy Sliding Mode Controller for Discrete Nonlinear Systems, International Journal of Computers Communications & Control Volume III No.1, 2008.