Abstract
Alertness is the state of attention by high sensory awareness. A lack of alertness is one of the main reasons of serious accidents. Traffic accidents caused by driver’s drowsy driving have a high fatality rate. This paper presents an EEG-based alertness detection system. In order to ensure the convenience and long-term wearing comfort of EEG recordings, the wearable electrode cap will be the principal choice in the future, and the selection of channels will be limited. We first built a 3-D simulated driving platform using Unity3D. Then, we perform an experiment with driving drift task. EEG signals are recorded form frontal and occipital regions. We select data segments using the driving reaction time, classify the state of alertness with a support vector machine (SVM), and select the optimal combination of channels with minimum number of channels. Our results demonstrate that alertness can be classified efficiently with one channel (PO6) at accuracy of 93.52%, with two channels (FP1+PO6) at 95.85% and with three channels (FP1+PO6+PO5 and FP1+PO6+POZ) at 96.11%.
Highlights
The issue of a decrease in the degree of alertness at work has received increasing attention due to the gradual increase of living pressure and work intensity [1]
It is observed that the effect of classification by using all channels is better than using single channel, and using part of channels is better than using all channels
In order to find the optimal combination of channels, we computed the classification accuracy of different combinations to evaluate the results [22]
Summary
The issue of a decrease in the degree of alertness at work has received increasing attention due to the gradual increase of living pressure and work intensity [1]. Alertness refers to a person's ability to maintain his focus of attention on performing an operational task for prolonged periods of time. The alertness level of a driver refers to the ability of the driver to perceive and understand the status of the car or the traffic conditions in a timely manner while driving, and to make the correct operation [2]. Staying alertness above a certain level is very important in some cases in our daily lives [1]. In the United States, as reported by the National Traffic Safety Administration, 60,000 traffic accidents take place due to pilot fatigue driving every year [5]. Detecting alertness level of the driver is of great significance to improve the driving features and the traffic safety situation
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