Zou Bing, Shen Mu, Li Xinhang, Zheng Yubo, Zhang Lin
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:248-251. doi: 10.1109/EMBC44109.2020.9175962.
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.
利用脑电图（EEG）信号准确可靠地检测驾驶疲劳是一种减少交通事故的方法。到目前为止，为了在使用EEG数据检测驾驶疲劳时获得相对较高的准确率，自然会将驾驶方向盘操作部分的数据剔除。然而，驾驶方向盘操作期间的数据段也包含有价值的信息。此外，驾驶方向盘操作是实际驾驶中的常见行为。在本研究中，我们利用驾驶方向盘操作部分的数据来检测疲劳。所使用的特征是根据数据计算出的频谱带功率。对于每个实验和每个实验参与者，数据和特征被划分为会话和受试者。利用划分后的特征，本工作对逻辑回归和多层感知器这两种分类方法进行跨会话和跨受试者验证与比较。为了比较效果，在驾驶方向盘操作和不操作方向盘的数据上都进行了实验。结果表明，两类数据的平均准确率之间的偏差仅为2.27%，并且使用多层感知器的效果比使用逻辑回归的效果好10.37%。这证明了驾驶方向盘操作期间的数据段也包含有效信息，可用于驾驶疲劳检测。
