|
Detection of driver drowsiness from EEG signals using wearable brain sensing headband |
|---|---|
| รหัสดีโอไอ | |
| Creator | 1. Khune Satt Nyein Chan 2. C. Srisurangkul 3. N. Depaiwa 4. S. Pangkreung |
| Title | Detection of driver drowsiness from EEG signals using wearable brain sensing headband |
| Publisher | Thai Society of Mechanical Engineers (TSME) |
| Publication Year | 2564 |
| Journal Title | Journal of Research and Applications in Mechanical Engineering (JRAME) |
| Journal Vol. | 9 |
| Journal No. | 2 |
| Page no. | JRAME-21-9-014 (p.1-9) |
| Keyword | Driver drowsiness detection, Electroencephalography (EEG), Brain-Computer Interface (BCI), Support Vector Machine (SVM), Artificial Neural Network (ANN) |
| URL Website | https://tci-thaijo.org/index.php/jrame/index |
| Website title | Journal of Research and Applications in Mechanical Engineering (JRAME) |
| ISSN | 2229-2152 |
| Abstract | Driver drowsiness detection plays an important role in the field of road safety and advanced driver assistance system. Electroencephalogram (EEG) signals are one of the most accurate and reliable indicators of fatigue and drowsiness but in the case of detecting drowsiness, its medical graded measuring system can be intrusive to the driver. The purpose of this research is to test the feasibility and usability of the consumer graded EEG sensor to use in a driver drowsiness detection system. The experiment wascarried out by using MUSE S brain sensing headband. Fast Fourier Transform (FFT) method was used to extract features from EEG signals. The extracted feature data are then used to build two classification model, the Support Vector Machine (SVM) and Artificial Neural Network (ANN). The detection of drowsiness is the binary classification task which is to classify between drowsy epochs and alert epochs. In the case of detecting only drowsy epochs, the SVM model detected 82.7% of the drowsy epochswhich was better than the ANN modelwhich can only detect 81.25% of the drowsy epochs.But in the detection of both drowsy and alert epochs, the ANN model performed better than that of SVM. The SVM model was tested with different kernel function and Fine Gaussian SVM model showed the highest accuracy of 87.8%. The ANN model performed slightly higher than the SVM model with an accuracy of 87.9%. The ability of consumer graded EEG sensor to use in drowsiness detection system was validated in this research. |
