EEG-based Driving Fatigue Detection during Operating the Steering Wheel Data Section.

Accurate and reliable detecting of driving fatigue using Electroencephalography (EEG) signals is a method to reduce traffic accidents. So far, it is natural to cut the part of operating the steering wheel data away for achieving the relatively high accuracy in detecting driving fatigue using EEG data. However, the data segment during operating the steering wheel also contains valuable information. Moreover, operating the steering wheel is a common practice during actual driving. In this study, we utilize the part of data operating the steering wheel to detecting fatigue. The feature used is the spectral band power calculates from the data. For each experiment and each experimental participant, the data and features are divided into sessions and subjects. Using the divided features, this work performs cross-session and cross-subject verification and comparison on the two classification methods of logistic regression and multi-layer perceptron. To compare the effect, the experiment is conducted on the data both operating the steering wheel and not operating the steering wheel. The result shows that the bias between the average accuracy of two types of data is only 2.27%, and the effect of using multi-layer perceptron is 10.37% better than using logistic regression. This proves that the data segment during operating the steering wheel also contains valid information and can be used for driving fatigue detection.