Classification of Driver Drowsiness Level using Wireless EEG

• Autorzy: Wali M. K. , Murugappan M. , Badlishah-Ahmad R.

Warianty tytułu

PL

Badania senności kierowcy na podstawie sygnału EEG

• Języki publikacji: EN

Abstrakty

EN

In this work, wireless Electroencephalogram (EEG) signals are used to classify the driver drowsiness levels (neutral, drowsy, high drowsy and sleep stage1) based on Discrete Wavelet Packet Transform (DWPT). Two statistical features (spectral centroid, and power spectral density) were extracted from four EEG frequency bands (delta, theta, alpha, and beta) using Fast Fourier Transform (FFT). These features are used to classify the driver drowsiness level using three classifiers namely, subtractive fuzzy clustering, probabilistic neural network, and K nearest neighbour. Results of this study indicates that the best average accuracy of 84.41% is achieved using subtractive fuzzy classifier based on power spectral density feature extracted by db4 wavelet function.

PL

W artykule zaprezentowano możliwość wykorzystania dyskretnej transformaty falkowej do analizy sygnału elektroencefalografii w badaniach senności kierowcy. Parametry statystyczne sygnału analizowano z wykorzystaniem dyskretnej transformaty Fouriera. Stwierdzono że najlepsza dokładność uzyskuje się stosując klasyfikator rozmyty i funkcję falkową db4.

Słowa kluczowe

EN

discrete wavelet transform; EEG; fast Fourier transform; fuzzy inference system

PL

dyskretna transformata falkowa; senność; dyskretna transformata Fouriera

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 6
• Strony: 113--117
• Opis fizyczny: Bibliogr. 15 poz., rys., tab., wykr.

Twórcy

autor

Wali M. K.

• School of Computer Engineering, University Malaysia Perlis

autor

Murugappan M.

• School of Computer Engineering, University Malaysia Perlis

autor

Badlishah-Ahmad R.

• School of Computer Engineering, University Malaysia Perlis

Bibliografia

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