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A Comparative Study of Drowsiness Detection From Eeg Signals Using Pretrained CNN Models
- Source :
- ICCCNT
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- Drowsiness has become one of the major causes of road accidents now-a-days. In order to alleviate this issue, a system has been developed, which uses electroencephalogram (EEG) signals to detect drowsiness with sufficient reliability. This experiment was conducted on a small population and the EEG signals were acquired using a 14-channel wireless headset, while they were in a virtual driving environment. To extract the eye closures, the EEG signal was segmented, and pre-processed. Further the scalograms which describes the time-frequency characteristics of these segments were taken. Pretrained Convolutional Neural Network based architectures viz. ResNet-152, ResNet101, VGG16, VGG19, AlexNet were used to distinguish three states of the driver namely “Sleepy or Drowsy”, “Asleep” and “Awake”.
Details
- Database :
- OpenAIRE
- Journal :
- 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)
- Accession number :
- edsair.doi...........245ac4aa2ff31fe8e817b52cbc4d9b3a