# Temporal EEG Imaging for Drowsy Driving Prediction
## 1 Introduction  
The prevention of drowsy driving has become a major challenge in safety driving issues. Many drivers experience driving in drowsy conditions, especially in long-term driving. Continuous, unexciting driving reduces the vigilance of drivers and increases the risk of traffic accidents. To address this problem, the development of brain-computer interfaces (BCIs) to investigate the human's cognitive state is an urgent necessity. Electroencephalography (EEG) is one of the most direct and effective physiological measures for the estimation of brain dynamics. Recent EEG studies have demonstrated that changes in alertness during driving are related to changes in global brain dynamics [1; 2]. It has also been shown that EEG is a robust measurement for the estimation of a driver's cognitive state [3; 4; 5; 6]. In addition, EEG provides abilities of convenient measurement in real timeand is therefore widely used in real applications [3; 7; 8; 9]. Although EEG has many advantages for the analysis of brain dynamics, the use of EEG-based BCIs in real applications remains challenging. The raw EEG signals acquired from the electrodes are often obscured by physiological artifacts such as eye movement and muscle movement, which is undesirable in the BCI system [10]. Therefore, removing these unwanted artifactsto capture brain activity has become a crucial issue in EEG-based BCI applications. Many studies have shown that independent component analysis (ICA) can effectively separate the artifacts from raw EEG data [11; 12; 13; 14]. The mixture signal is decomposed into many statistically independent components by ICA. A non-artifact signal is obtained by excluding the components that are associated with artifacts. Although ICA is a powerful tool for extracting brain activity from raw EEGsignal, it cannot support real-time applications because the separated artifacts need to be removed manually. This drawback limits the utility of ICA forreal-world BCI applications. An automatic processing BCI is strongly required for drowsy driving prediction since traffic accidents always occur in a very short time. Therefore, this study does not apply any artifact removal process to the raw EEG data during the experiment, ensuring that the proposed method does not use manual processes for the drowsy driving prediction task. For EEG signals without artifact removal, how to correctly extract the informative features of EEG signals becomes a major challenge in BCI applications. A popular approach for feature extraction is transforming the EEG signals into a frequency domain [15; 16]. Fast Fourier transform (FFT) is applied to compute the power spectra of the multi-channel time-series EEG signals into the frequency domain; then, the average of the power spectra value for each frequency is collected to obtain a feature vector for classification [3; 17]. The main disadvantage of such an approach is that it only considers the frequency information. EEG is measured over the scalp in a three-dimensional space. It is obvious that the spatial information of EEG cannot be well described by a feature vector. Instead of the 1D feature vector, there is an increasing trend to use 2D feature maps for the analysis of EEG, which have achieved good performance in their application areas [18; 19]. As the most popular machine learning technique in recent years, deep learning has achieved significant success in a variety of research fields, such as speech [20], images [21; 22; 23] and video [24]. The ability of deep learning techniques to learn unknown features from incoming data has gained considerable attention in EEG studies [25; 26; 27]. There is an increasing trend to use convolutional neural networks (CNNs) to analyze EEGs due to their state-of-the-art performance in the computer vision field. A popular approach is transforming the EEG measurement into a 2D feature map and then passing it into a CNN model for classification [28; 29; 30; 31].