Department of Electrical and Electronic Engineering, Institute of Health and Analytics, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia.
Department of Informatics Engineering, Faculty of Engineering, Universitas Islam Riau, Tembilahan 28284, Indonesia.
Sensors (Basel). 2022 Jun 24;22(13):4764. doi: 10.3390/s22134764.
Drowsiness is one of the main causes of road accidents and endangers the lives of road users. Recently, there has been considerable interest in utilizing features extracted from electroencephalography (EEG) signals to detect driver drowsiness. However, in most of the work performed in this area, the eyeblink or ocular artifacts present in EEG signals are considered noise and are removed during the preprocessing stage. In this study, we examined the possibility of extracting features from the EEG ocular artifacts themselves to perform classification between alert and drowsy states. In this study, we used the BLINKER algorithm to extract 25 blink-related features from a public dataset comprising raw EEG signals collected from 12 participants. Different machine learning classification models, including the decision tree, the support vector machine (SVM), the K-nearest neighbor (KNN) method, and the bagged and boosted tree models, were trained based on the seven selected features. These models were further optimized to improve their performance. We were able to show that features from EEG ocular artifacts are able to classify drowsy and alert states, with the optimized ensemble-boosted trees yielding the highest accuracy of 91.10% among all classic machine learning models.
困倦是道路交通事故的主要原因之一，危及道路使用者的生命。最近，人们对利用脑电图 (EEG) 信号中提取的特征来检测驾驶员困倦产生了相当大的兴趣。然而，在该领域进行的大多数工作中，脑电图信号中存在的眨眼或眼动伪迹被视为噪声，并在预处理阶段被去除。在这项研究中，我们研究了从 EEG 眼动伪迹本身提取特征以执行警觉和困倦状态之间分类的可能性。在这项研究中，我们使用 BLINKER 算法从一个包含 12 名参与者采集的原始 EEG 信号的公共数据集提取 25 个与眨眼相关的特征。基于七个选定的特征，训练了不同的机器学习分类模型，包括决策树、支持向量机 (SVM)、K-最近邻 (KNN) 方法和袋装和增强树模型。我们进一步优化了这些模型以提高其性能。我们能够表明，来自 EEG 眼动伪迹的特征能够对困倦和警觉状态进行分类，在所有经典机器学习模型中，优化后的集成增强树模型的准确率最高，达到 91.10%。
