## 1 Introduction
They stated that their Artificial Neural Network (ANN)-based classification system had an accuracy of 83%. Another feature extraction method using Approximation Entropy (AE) and Sample Entropy (SE) was applied to EEG signals by [PERSON] et al. [14] to predict driver fatigue. This method was carried out on fifty people. They used the SVM for classification, and their proposed method achieved a classification accuracy of 90% with two categories of driver weariness. Furthermore, driver fatigue was predicted using EEG data by [PERSON] et al. [15] using an autoregressive model. In this study, 48 people participated with two fatigue levels. They employed Independent Component Analysis (ICA) to reduce the feature vector size as part of their investigation. They deployed a Bayesian classifier which achieved an accuracy of 88.2%. Another type of feature extraction from EEG signals based on Fuzzy Entropy (FE) is proposed by [PERSON] et al. [16] They tested their method on six participants to identify two distinct states of driving fatigue using an ANN classifier which is reported to be 90%. [PERSON] et al. [17] used AE, SE, and FE to extract the features from EEG data to evaluate driver fatigue. In their study, eight people volunteered to be assessed using an AdaBoost classifier with 92% accuracy. [PERSON] et al. [18] used the AE, SE, and FE which extracted features of EEG signals to identify fatigue. Their sensitivity, specificity, and accuracy for classifying data were all 100% when using the ANN classifier. Trigonometric transformation techniques are involved in feature extraction. [PERSON] et al. [19] extracted Frequency information from EEG signals using the FFT to detect the driver's state of weariness. Fifty participants used Virtual Reality (VR) equipment during the trials. According to proponents, the LR model reliably classified data at a 90% rate. Another effort is proposed by [PERSON] et al. [20] to extract components from EEG recordings and they used Power Spectral Density (PSD) to perform this task. Their classification accuracy is 83% using the LR model. [PERSON] et al. [21] used FFT to extract frequency information from EEG recordings to distinguish between two states of driver fatigue. They analyzed data from twenty individuals with a reliability in classifying objects of 85%. [PERSON] et al. [22] extracted numerous time-frequency and statistical metrics from EEG data, such as mean, standard deviation, and correlation with FFT, to determine whether or not a driver was fatigued. Thirty people were tested using the Emotiv EPOC+ (EEG based device \"[[https://www.emotiv.com/epoc/](https://www.emotiv.com/epoc/)]([https://www.emotiv.com/epoc/](https://www.emotiv.com/epoc/))\" (accessed on 29 November 2022)) to identify two distinct phases of driver fatigue. They boast a 96% accuracy rate when it enrolled into a classification task. Another approach is proposed by [PERSON] et al. [23]. They used a combination of adaptive scaling factor and entropy characteristics using two channels of EEG signals (Fp1 and Fp2). They achieved 40% on a two-stage classification of driver fatigue. In addition, the EEG toolbox was also used to filter out unwanted EOG activity. This task enhanced the system's performance with a classification accuracy of 98%. [PERSON] et al. [24] turned to deep neural networks (DNNs) with testing on ten participants. They used 11 convolutional layers to create their deep network. They achieved a classification rate of 95%. A combination of EEG signals, facial expressions, and gyroscope data is proposed by [PERSON] et al. [25]. They proposed a system based on DNNs, which achieved an accuracy of 93%. It is clear from the works discussed that many feature extraction methods were employed to extract the key features from the input signals. Furthermore, the classification task utilized several strategies to reach the optimal detection accuracy. However, they reached a maximum detection accuracy of 95% using powerful feature extractors and classifiers.