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. 2018 Aug;12(4):365-376.
doi: 10.1007/s11571-018-9481-5. Epub 2018 Feb 21.

A novel real-time driving fatigue detection system based on wireless dry EEG

Hongtao Wang  1   2 , Andrei Dragomir  1 , Nida Itrat Abbasi  1   3 , Junhua Li  1 , Nitish V Thakor  1 , Anastasios Bezerianos  1
Affiliations

A novel real-time driving fatigue detection system based on wireless dry EEG

Hongtao Wang et al. Cogn Neurodyn. 2018 Aug.

Abstract

Development of techniques for detection of mental fatigue has varied applications in areas where sustaining attention is of critical importance like security and transportation. The objective of this study is to develop a novel real-time driving fatigue detection methodology based on dry Electroencephalographic (EEG) signals. The study has employed two methods in the online detection of mental fatigue: power spectrum density (PSD) and sample entropy (SE). The wavelet packets transform (WPT) method was utilized to obtain the θ (4-7 Hz), α (8-12 Hz) and β (13-30 Hz) bands frequency components for calculating corresponding PSD of the selected channels. In order to improve the fatigue detection performance, the system was individually calibrated for each subject in terms of fatigue-sensitive channels selection. Two fatigue-related indexes: ( θ+α )/ β and θ / β were computed and then fused into an integrated metric to predict the degree of driving fatigue. In the case of SE extraction, the mean of SE averaged across two EEG channels ('O1h' and 'O2h') was used for fatigue detection. Ten healthy subjects participated in our study and each of them performed two sessions of simulated driving. In each session, subjects were required to drive simulated car for 90 min without any break. The results demonstrate that our proposed methods are effective for fatigue detection. The prediction of fatigue is consistent with the observation of reaction time that was recorded during simulated driving, which is considered as an objective behavioral measure.

Keywords: Channel selection; Driving fatigue; Dry electrodes; Electroencephalogram; PSD and entropy.

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Figures

Fig. 1
Fig. 1
a A scenery of the road captured from the screen, the guide car breaks randomly with the lings flashing. b The simulated driving system used for the implementation of the proposed protocol
Fig. 2
Fig. 2
The names and positions of the 24 Dry-EEG electrodes. The two channels marked in red are used for sample entropy calculation. (Color figure online)
Fig. 3
Fig. 3
The flow chart of the proposed two driving fatigue prediction algorithms
Fig. 4
Fig. 4
Wavelet packets transform (WPT) decomposition over six levels
Fig. 5
Fig. 5
a, b Are the (PSDθ+PSDα)/PSDβ and PSDθ/PSDβ fatigue index for fatigue-sensitive channels of subject 1 respectively
Fig. 6
Fig. 6
The first and second rows present the topographic map of fatigue-related index (PSDθ+PSDα)/PSDβ constructed through integrating the channel-based fatigue index, means of the every 10 mins of subject 1. The third and fourth rows present the topographic map of fatigue-related index PSDθ/PSDβ) constructed through integrating the channel-based fatigue index, means of the every 10 mins of subject 1
Fig. 7
Fig. 7
Power spectrum density integrated metrics for driving fatigue prediction during the online experiment of 5400 s (90 mins) for subject 1 to subject 10
Fig. 8
Fig. 8
Sample entropy fatigue index for driving fatigue prediction during the online experiment of 5400 s (90 mins) for subject 1 to subject 10
Fig. 9
Fig. 9
Mean value and standard deviation of the NASA-TLX scores given by the ten subjects
Fig. 10
Fig. 10
Effects of time-on-task on behavioral performance in terms of reaction time. Mean and standard deviation of reaction time were calculated for the driving fatigue detection experiment with a non-overlapping 10-min bin
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