# Temporal EEG Imaging for Drowsy Driving Prediction
## 5 Discussion and Conclusions  
It is challenging to classify EEG data without an artifact removal process because drivers' brain activity can change over time due to many factors, such as their mental state and body movement, which result in the temporal fluctuations of the EEG signals. However, there still contains important information associated with the drowsiness level of drivers, and thus, the temporal analysis of the EEG signals becomes a crucial issue. This study proposes a temporal EEG image algorithm that combines a sequence of EEG images to form a new EEG image that contains brain dynamics from multiple time points. Our experimental results show that the proposed method achieves good performance in the drowsiness prediction.  
Support vector machines (SVMs) are also employed for comparison with our approach because they are popular classifiers for EEG analysis. In our experiment, the computational cost is expensive and a bad prediction result is obtained if we use the EEG image as the input of SVM. Thus, the power spectrum of EEG is selected as the input of the SVM. Similar to the experiment described in the previous  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{3}{c}{**EEGnet [45]**} & \multicolumn{3}{c}{**HCNN [28]**} \\ \cline{2-7}  & **Alert (\%)** & **Drowsy (\%)** & **Total Accuracy (\%)** & **Alert (\%)** & **Drowsy (\%)** & **Total Accuracy (\%  
sections, the input data do not apply any artifact removal process. The experimental results indicate that SVM provides a biased prediction result towards the alert class. That is, it always predicts an alert regardless of the input and results in a perfect detection of alert trials but no detection of drowsy trials. We found that SVM only provides meaningful prediction results in a balanced training dataset that has a similar number of alert trials and drowsy trials. In real-world applications, BCI systems usually have to perform drowsiness prediction under imbalanced-datasets, which means that SVM cannot provide reasonable reliability in real-world BCI applications.  
To find a suitable CNN model for the drowsiness prediction task, this study introduces two CNN architectures for further evaluation: (1) AlexNet--a very popular CNN architecture that is larger than the CNN architecture used in this study [18], (2) 3D CNN--by performing 3D convolutions, which is capable of learning features from both spatial and temporal dimensions. For AlexNet, the EEG measurement is transformed into \(227\times 227\) to fit the input size of AlexNet. For 3D CNN, different from our approach using a linear combination of a sequence of EEG images, these EEG images are directly fed into the 3D CNN model since they are 3D input data. Our results show that both AlexNet and 3D CNN cannot achieve better performance than the proposed CNN architecture in the present study. That is, high-complexity CNN is not required for the drowsiness prediction task.  
The detection of drowsy trials remains a challenge because different subjects have different drowsy EEG patterns. A further investigation of brain dynamics is the key to improving the prediction performance of the BCI system.  
[PERSON] conceived the main idea and wrote the paper. [PERSON]-[PERSON] provided the academic support and checked the manuscript, [PERSON]-[PERSON] provided the funding support and performed the experiment.  
This work was supported in part by the Australian Research Council under Grant DP180100670 and Grant DP180100656, Taiwan Ministry of Science and Technology under AQ1 Grant MOST 106-2218-E-009-027-MY3 and MOST 108-2221-E-009-120-MY2 and W911 NF-10-2-0022.  
The authors declare no conflict of interests.