Wang Hongtao, Dragomir Andrei, Abbasi Nida Itrat, Li Junhua, Thakor Nitish V, Bezerianos Anastasios
1Singapore Institute for Neurotechnology(SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore, 117456 Singapore.
2School of Information Engineering, Wuyi University, Jiangmen, 529020 Guangdong China.
Cogn Neurodyn. 2018 Aug;12(4):365-376. doi: 10.1007/s11571-018-9481-5. Epub 2018 Feb 21.
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 () and sample entropy (SE). The wavelet packets transform (WPT) method was utilized to obtain the (4-7 Hz), (8-12 Hz) and (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: ( )/ and / 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.
精神疲劳检测技术的发展在诸如安保和交通运输等需要持续保持注意力的关键领域有着广泛应用。本研究的目的是基于干式脑电图（EEG）信号开发一种新型的实时驾驶疲劳检测方法。该研究在精神疲劳的在线检测中采用了两种方法：功率谱密度（PSD）和样本熵（SE）。利用小波包变换（WPT）方法获取4 - 7赫兹、8 - 12赫兹和13 - 30赫兹频段的频率成分，以计算所选通道的相应PSD。为了提高疲劳检测性能，该系统针对每个受试者在疲劳敏感通道选择方面进行了单独校准。计算了两个与疲劳相关的指标：（PSD（4 - 7赫兹））/（PSD（13 - 30赫兹））和SE（O1h）/SE（O2h），然后将它们融合成一个综合指标来预测驾驶疲劳程度。在提取SE的情况下，使用两个EEG通道（“O1h”和“O2h”）上SE的平均值进行疲劳检测。十名健康受试者参与了我们的研究，他们每人进行了两阶段的模拟驾驶。在每个阶段，受试者被要求驾驶模拟汽车90分钟，中途不得休息。结果表明，我们提出的方法对于疲劳检测是有效的。疲劳预测与模拟驾驶期间记录的反应时间观测结果一致，反应时间被视为一种客观的行为测量指标。
