Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

Abstract

Driver fatigue has become one of the major causes of traffic accidents, and is a complicated physiological process. However, there is no effective method to detect driving fatigue. Electroencephalography (EEG) signals are complex, unstable, and non-linear; non-linear analysis methods, such as entropy, maybe more appropriate. This study evaluates a combined entropy-based processing method of EEG data to detect driver fatigue. In this paper, 12 subjects were selected to take part in an experiment, obeying driving training in a virtual environment under the instruction of the operator. Four types of enthrones (spectrum entropy, approximate entropy, sample entropy and fuzzy entropy) were used to extract features for the purpose of driver fatigue detection. Electrode selection process and a support vector machine (SVM) classification algorithm were also proposed. The average recognition accuracy was 98.75%. Retrospective analysis of the EEG showed that the extracted features from electrodes T5, TP7, TP8 and FP1 may yield better performance. SVM classification algorithm using radial basis function as kernel function obtained better results. A combined entropy-based method demonstrates good classification performance for studying driver fatigue detection.

Noise Robustness Analysis of Performance for EEG-Based Driver Fatigue Detection Using Different Entropy Feature Sets

“…In the above-mentioned four types of entropies, AE, SE and FE have variable parameters, m and r. In the present study, m = 2 while r = 0.2*SD, where SD denotes the standard deviation of the time series as per the literature [3,22,25].…”

“…It should be noted that the validation of the fatigue condition was also based on a self-reported fatigue questionnaire as per Borg's fatigue scale and Lee's subjective fatigue scale [26,27]. This method of using a questionnaire to identify the fatigue condition has not only been used in our study, but also in many other studies [2,3]. The drivers were required to complete all tasks and ensure safe driving.…”

“…Combined entropy has been employed to achieve a better performance [3], but questions remain as to the impact on the detection performance if noise was superimposed on some channels. Combined feature sets (FE + SE + AE + PE) of EEG signals were extracted for training and recognition, before gradually adding noise.…”

“…Mu et al [2] employed fuzzy entropy for feature extraction and an SVM classifier to achieve an average accuracy of 85%. Other results from their study showed that four feature sets (SE, AE, PE, and FE) and SVM were proposed, with an average accuracy of 98.75% [3]. Fu et al [4] proposed a fatigue detection model based on the Hidden Markov Model (HMM), and achieved a highest accuracy of 92.5% based on EEG signals and other physiological signals.…”

Cepstrum Coefficient Analysis from Low-Frequency to High-Frequency Applied to Automatic Epileptic Seizure Detection with Bio-Electrical Signals

“…In the past and at present, well-trained and experienced neurophysiologists are employed universally to detect epileptic seizures through conventional methods by inspecting EEG signals over a long period [6,9,10,[13][14][15][16]. Practically, to ensure high accuracy, detailed evaluations are essential, such as brain imaging tests like magnetic resonance imaging (MRI), computed tomography (CT), long-term EEG tests, blood tests, and medical history examination [15,[17][18][19][20][21][22][23][24]. All of these evaluations are costly, which places a heavy financial burden on patients, especially those living in areas with frequent epileptic seizure breakout and poverty.…”