Abstract
The proposed work aims at providing an optimized method for determining driver status by analyzing Electroencephalogram (EEG) signals by time and frequency domain analysis using wavelet transforms. Human brain alertness level can be detected by direct measurement of electrical activity inside the brain using EEG Signals. These signals are acquired from electrodes placed on scalp. Such signal would be usually contaminated with various artifacts like muscle movements. Therefore the noise from the raw signals acquired from electrodes is removed using suitable band pass filter. Filtered signals are further subjected to discrete wavelet transform for isolating EEG rhythms. EEG rhythms include five frequency bands namely (delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz) and gamma (>30 Hz). Debauches DB8 wavelet transform is used for decomposing the signal in to eight levels and lower five frequency bands are considered for the analysis. For the proposed work sleep data sets from Physionet are used for analysis.
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Nissimagoudar, P.C., Nandi, A.V., Gireesha, H.M. (2019). EEG Signal Analysis Using Wavelet Transform for Driver Status Detection. In: Abraham, A., Gandhi, N., Pant, M. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2018. Advances in Intelligent Systems and Computing, vol 939. Springer, Cham. https://doi.org/10.1007/978-3-030-16681-6_6
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