Arif Saad, Khan Muhammad Jawad, Naseer Noman, Hong Keum-Shik, Sajid Hasan, Ayaz Yasar
School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology, Islamabad, Pakistan.
National Center of Artificial Intelligence (NCAI), Islamabad, Pakistan.
Front Hum Neurosci. 2021 Apr 30;15:658444. doi: 10.3389/fnhum.2021.658444. eCollection 2021.
A passive brain-computer interface (BCI) based upon functional near-infrared spectroscopy (fNIRS) brain signals is used for earlier detection of human drowsiness during driving tasks. This BCI modality acquired hemodynamic signals of 13 healthy subjects from the right dorsolateral prefrontal cortex (DPFC) of the brain. Drowsiness activity is recorded using a continuous-wave fNIRS system and eight channels over the right DPFC. During the experiment, sleep-deprived subjects drove a vehicle in a driving simulator while their cerebral oxygen regulation (CORE) state was continuously measured. Vector phase analysis (VPA) was used as a classifier to detect drowsiness state along with sleep stage-based threshold criteria. Extensive training and testing with various feature sets and classifiers are done to justify the adaptation of threshold criteria for any subject without requiring recalibration. Three statistical features (mean oxyhemoglobin, signal peak, and the sum of peaks) along with six VPA features (trajectory slopes of VPA indices) were used. The average accuracies for the five classifiers are 90.9% for discriminant analysis, 92.5% for support vector machines, 92.3% for nearest neighbors, 92.4% for both decision trees, and ensembles over all subjects' data. Trajectory slopes of CORE vector magnitude and angle: (||) and (∠) are the best-performing features, along with ensemble classifier with the highest accuracy of 95.3% and minimum computation time of 40 ms. The statistical significance of the results is validated with a -value of less than 0.05. The proposed passive BCI scheme demonstrates a promising technique for online drowsiness detection using VPA along with sleep stage classification.
一种基于功能近红外光谱（fNIRS）脑信号的被动式脑机接口（BCI）被用于在驾驶任务期间更早地检测人的困倦状态。这种BCI模式从大脑右侧背外侧前额叶皮层（DPFC）采集了13名健康受试者的血液动力学信号。使用连续波fNIRS系统和右侧DPFC上的八个通道记录困倦活动。在实验过程中，睡眠不足的受试者在驾驶模拟器中驾驶车辆，同时持续测量他们的脑氧调节（CORE）状态。矢量相位分析（VPA）被用作分类器来检测困倦状态以及基于睡眠阶段的阈值标准。使用各种特征集和分类器进行了广泛的训练和测试，以证明无需重新校准即可针对任何受试者调整阈值标准的合理性。使用了三个统计特征（平均氧合血红蛋白、信号峰值和峰值总和）以及六个VPA特征（VPA指数的轨迹斜率）。五个分类器对所有受试者数据的平均准确率分别为：判别分析90.9%、支持向量机92.5%、最近邻92.3%、决策树92.4%以及集成分类器。CORE矢量幅度和角度的轨迹斜率：（||）和（∠）是表现最佳的特征，集成分类器的准确率最高，为95.3%，最小计算时间为40毫秒。结果的统计显著性通过小于0.05的p值得到验证。所提出的被动式BCI方案展示了一种使用VPA进行在线困倦检测以及睡眠阶段分类的有前景的技术。
