我們比較我們篩選出來的通道數，和當代 fMRI 觀察疲勞對於腦袋中的影響，兩者 皆顯示疲勞跟大腦的運動皮層有關。我們也和當代不同腦電圖特徵選擇應用方法比較， 並顯示我們的方法不論是在準確度還是減少的通道數都是比大多數的研究優秀。 ;Driver fatigue is an underestimated factor that leads to traffic accidents. This research reviewed researches and found out how drowsiness will impact driving performance in various ways. Increasing lane drifting, decreasing steering wheel controlling ability, increasing reaction time (RT), and poor speed control, are the signs and indicators for drivers to discover they are not suitable to operate the vehicle. We then reviewed the four most common ways to detect drowsiness. Various drowsiness detection methods this research reviewed, drowsiness detection via EEG has the highest accuracy. However, due to the multi-channel nature of the EEG signal, using EEG detection in everyday life nowadays is not possible. Our objective is to reduce the channel needed so that we could make EEG drowsiness detection useful in daily life.
First, this research looks into different ways to analyze EEG signal. This research inspects two feature selection methods, filter method and wrapper method. We also discussed two classification model, which is the widely used support vector machine (SVM) and the long short term model (LSTM). Our method introduced the feature selection method into drowsiness detection with the key idea of reducing the redundancies between 40 EEG channels with the combination of filter method and LSTM classifier. The result is that we obtain the accuracy of 94.9 % in 7 channel Pearson’s correlation method and 94.04 % in 6 channel mutual information method.
This research then evaluates the selected channel with some drowsiness research, which suggests that when it comes to drowsiness, this research should look into one’s motor cortex of the brain. This research compares our result to some state-of-the-art method in different EEG applications which show great results in both accuracy and the amount of channel reduced.
