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A transfer learning approach for drowsiness detection from EEG signals

Publication Type : Conference Paper

Publisher : Springer Singapore

Source : Innovations in Computational Intelligence and Computer Vision: Proceedings of ICICV 2020 Pages 369-375, 2021

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Campus : Amritapuri

School : School of Engineering

Department : Electronics and Communication

Year : 2021

Abstract : There has been an alarming increase in the number of accidents that occur due to drowsiness. Research so as to analyze and rectify this state has become a necessity. Here, a system has been developed which would detect drowsiness with sufficient reliability using electroencephalogram (EEG) signals. The study was conducted on nine males and nine females, whose EEG waveforms are obtained with the help of a wireless neuro-headset, while subjects underwent a virtual driving experiment. Preprocessing of the raw EEG signals is done by Principal Component Analysis (PCA). The resultant signals are then normalized and smoothed. Subsequently, the preprocessed signal is segmented and the time-frequency features are extracted using Continuous Wavelet Transform (CWT). Transfer learning approach is adopted in this paper to discriminate between the 3 states, ‘Drowsy’, ‘Asleep’ and ‘Awake’. An existing pre-trained ResNet50 model, fine-tuned using the scalograms obtained from CWT, is utilized for evaluating the state of driver

Cite this Research Publication : Poorna, S.S., Deep, A., Hariharan, K., Jain, R.R., Krishnan, S. (2021). A Transfer Learning Approach for Drowsiness Detection from EEG Signals. In: Sharma, M.K., Dhaka, V.S., Perumal, T., Dey, N., Tavares, J.M.R.S. (eds) Innovations in Computational Intelligence and Computer Vision. Advances in Intelligent Systems and Computing, vol 1189. Springer, Singapore.

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