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A comparative study of drowsiness detection from Eeg signals using pretrained CNN models

Publication Type : Conference Paper

Publisher : IEEE

Source : 2021 12th international conference on computing communication and networking technologies (ICCCNT), Pages 1-3, 2021

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Campus : Amritapuri

School : School of Engineering

Department : Electronics and Communication

Year : 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”.

Cite this Research Publication : B. V. Bharath Chandra, C. Naveen, M. M. Sampath Kumar, M. S. Sai Bhargav, S. S. Poorna and K. Anuraj, "A Comparative Study of Drowsiness Detection From Eeg Signals Using Pretrained CNN Models," 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2021, pp. 1-3, doi: 10.1109/ICCCNT51525.2021.9579555.

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