Abstract
Forewarning the onset of drowsiness in drivers and pilots by analyzing the state of brain can reduce the number of road and aviation accidents to a large extent. For this, EEG signals are acquired using a 14-channel wireless neuro-headset, while subjects are in virtual driving environment. Principal component analysis (PCA) of EEG data is used to extract the dominant ocular pulses. Two sets of feature vectors obtained from the analysis are: one set characterizing eye blinks only and another set where eye blinks are excluded. The temporal characteristics of ocular pulses are obtained from the first set. The latter is obtained from the spectral bands delta, theta, alpha, beta, and gamma. Classification using K-nearest neighbor (KNN) and artificial neural network (ANN) gives an accuracy of 80% and 85%, sensitivity of 33.35% and 58.21%, respectively, for these features. The targets used for classification are alert or awake, drowsy, and sleep state.
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Acknowledgements
We wish to thank Almighty God for the successful completion of this work. We also thank Amrita Vishwa Vidyapeetham, Amritapuri Campus, Kerala, for providing us support to carry out this project and permission to publish this work.
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Poorna, S.S., Arsha, V.V., Aparna, P.T.A., Gopal, P., Nair, G.J. (2018). Drowsiness Detection for Safe Driving Using PCA EEG Signals. In: Pattnaik, P., Rautaray, S., Das, H., Nayak, J. (eds) Progress in Computing, Analytics and Networking. Advances in Intelligent Systems and Computing, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-7871-2_40
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DOI: https://doi.org/10.1007/978-981-10-7871-2_40
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