Abstract
Electrical signal generated by the brain represents not only the brain function but also the status of the whole body. This paper focuses on finding the relation between EEG signal and human drowsiness, for which we require efficient algorithms. In the drowsiness state, a decrease of vigilance is generally observed. Identification was done by giving the preprocessed signal to a trained ANN to identify correctly the sleep condition of the person under observation. Different back-propagation algorithms are used for the study and the best one chosen by using the MSE estimation. Then using this system, classification is done and the drowsy signal sample is identified from given input samples.
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References
S. Sanei, J. Chambers, EEG Signal Processing (Wiley, NJ, 2007)
M.S. Amin, M.R. Azim, F.M. Hasan, Spectral Analysis of Human Sleep EEG Signal, in 2nd International Conference on Signal Processing System ICPS (2010)
A. Vuckovic, V. Radivojevic, A.C.N. Chen, Automatic recognition of alertness and drowsiness from EEG by an artificial neural network. Med. Eng. Phys. 24(5), 349–360 (2002)
A. Hyvarinen, E. Oja, Independent component analysis: algorithm and applications. Neural Netw. 13(4–5), 411–430 (2000)
Ella Bingham, A. Hyvarinen, A fast fixed point algorithm for Independent component analysis of complex valued signals. Int. J. Neural Syst. 10(1), 1–8 (2000)
T. Tanaka, Y. Saito, Rhythmic component extraction for multichannel EEG data analysis. ICASSP 2008, 425–428 (2008)
S. Monto, S. Palva, J. Voipio, J.M. Palva, Very slow EEG fluctuation predict the dynamics of stimulus detection and oscillation of amplitude in humans. J. Neurosci. 28(33), 8272–8286 (2008)
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Jai, J., Titus, G., Purushothaman, S. (2015). EEG-based Automatic Detection of Drowsy State. In: Suresh, L., Dash, S., Panigrahi, B. (eds) Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. Advances in Intelligent Systems and Computing, vol 324. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2126-5_8
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DOI: https://doi.org/10.1007/978-81-322-2126-5_8
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Publisher Name: Springer, New Delhi
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