Detecting Unfavorable Driving States in Electroencephalography Based on a PCA Sample Entropy Feature and Multiple Classification Algorithms

Abstract

Unfavorable driving states can cause a large number of vehicle crashes and are significant factors in leading to traffic accidents. Hence, the aim of this research is to design a robust system to detect unfavorable driving states based on sample entropy feature analysis and multiple classification algorithms. Multi-channel Electroencephalography (EEG) signals are recorded from 16 participants while performing two types of driving tasks. For the purpose of selecting optimal feature sets for classification, principal component analysis (PCA) is adopted for reducing dimensionality of feature sets. Multiple classification algorithms, namely, K nearest neighbor (KNN), decision tree (DT), support vector machine (SVM) and logistic regression (LR) are employed to improve the accuracy of unfavorable driving state detection. We use 10-fold cross-validation to assess the performance of the proposed systems. It is found that the proposed detection system, based on PCA features and the cubic SVM classification algorithm, shows robustness as it obtains the highest accuracy of 97.81%, sensitivity of 96.93%, specificity of 98.73% and precision of 98.75%. Experimental results show that the system we designed can effectively monitor unfavorable driving states.

Research on Driving Fatigue Alleviation Using Interesting Auditory Stimulation Based on VMD-MMSE

“…SampEn is suitable for feature detection in small sample datasets and has little dependence on data length, which is suitable for real-time online detection [40]. Studies have shown that the SampEn method is able to extract fatigue features from EEGs [63,64].…”

“…Additionally, as a non-linear processing method, entropy is also widely used in the detection of driving fatigue [36][37][38]. Compared with the mutlifractality method [39,40], MMSE can extract EEG features on a time scale, and the data length required by the MMSE method is shorter in terms of EEG processing. Consequently, for the special situation of driving fatigue detection, the MMSE method can better meet the needs of traffic safety.…”

Motion Recognition Based on Sum of the Squared Errors Distribution

“…( ) = { (0), (1), (2) … ( )} (14) where ( ) = ( ( ) , ( ), ( )) (0 ≤ ≤ ). Two kinds of errors are encountered during data collection: 1.…”

“…However, there are some limitations, such as the camera's field of view, the complexity of target background, the intensity of light, and the privacy of the user. With the development of sensor technology, non-visual motion recognition technology has been developed rapidly, such as Hidden Markov Model (HMM) [12], Support Vector Machine (SVM) [13], Principal component analysis (PCA) [14], K-Nearest Neighbor (KNN) [15], Neural Network (NN), Singular value decomposition (SVD), and Wavelet packet transform (WPT). Most of these algorithms use surface electromyography (SEMG) signal acquisition sensor and inertial sensors in the process of data collection.…”

Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

“…In a real-time environment, it is important to detect and monitor driver behavior to save human lives. To resolve this problem, there were many automatic driver fatigue detection systems [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ] developed in past studies. Several computer vision-based applications were developed in the past to detect and predict driver fatigue.…”