Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau.
Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Paipa, Macau.
Physiol Meas. 2021 May 13;42(4). doi: 10.1088/1361-6579/abf336.
. Although various driving fatigue detection strategies have been introduced, the limited practicability is still an obstacle for the real application of these technologies. This study is based on the newly proposed non-hair-bearing (NHB) method to achieve practical driving fatigue detection with fewer channels from NHB areas and more efficient electroencephalogram (EEG) features.. EEG data were recorded from 20 healthy subjects (15 males, age = 22.2 ± 3.2 years) in a 90 min simulated driving task using a remote wireless cap. Behaviorally, subjects demonstrated a salient fatigue effect, as reflected by a monotonic increase in reaction time. Using a sliding-window approach, we determined the vigilant and fatigued states at individual level to reduce the inter-subject differences in behavioral impairment and brain activity. Multiple EEG features, including power-spectrum density (PSD), functional connectivity (FC), and entropy, were estimated in a pairwise manner, which were set as input for fatigue classification.. Intriguingly, this data-driven approach showed that the best classification performance was achieved using three EEG channel pairs located in the NHB area. The mixed features of the frontal NHB area lead to the high within-subject detection rate of driving fatigue (92.7% ± 0.92%) with satisfactory generalizability for fatigue classification across different subjects (77.13% ± 0.85%). Moreover, we found the most prominent contributing features were PSD of different frequency bands within the frontal NHB area and FC within the frontal NHB area and between frontal and parietal areas.. In summary, the current work provided objective evidence to support the effectiveness of the NHB method and further improved the performance, thereby moving a step forward towards practical driving fatigue detection in real-world scenarios.
. 虽然已经提出了各种驾驶疲劳检测策略，但这些技术的实际应用仍然受到实用性有限的限制。本研究基于新提出的非毛发区（NHB）方法，使用更少的 NHB 区域通道和更有效的脑电图（EEG）特征来实现实际驾驶疲劳检测。。从 20 名健康受试者（15 名男性，年龄=22.2±3.2 岁）中记录了在使用远程无线帽的 90 分钟模拟驾驶任务期间的 EEG 数据。行为上，受试者表现出明显的疲劳效应，表现为反应时间呈单调增加。使用滑动窗口方法，我们在个体水平上确定了警觉和疲劳状态，以减少行为障碍和大脑活动的个体间差异。使用成对方式估计了包括功率谱密度（PSD）、功能连接（FC）和熵在内的多种 EEG 特征，这些特征被设置为疲劳分类的输入。。有趣的是，这种数据驱动的方法表明，使用位于 NHB 区域的三个 EEG 通道对可以实现最佳的分类性能。来自额叶 NHB 区域的混合特征导致驾驶疲劳的个体内检测率很高（92.7%±0.92%），并且对不同受试者的疲劳分类具有良好的可推广性（77.13%±0.85%）。此外，我们发现最突出的贡献特征是额叶 NHB 区域内不同频带的 PSD 和额叶 NHB 区域内以及额叶和顶叶区域之间的 FC。。总之，本研究为 NHB 方法的有效性提供了客观证据，并进一步提高了性能，从而在实际场景中实现实际驾驶疲劳检测又迈进了一步。
