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Subtractive fuzzy classifier based driver distraction levels classification using EEG.

Wali MK, Murugappan M, Ahmad B - J Phys Ther Sci (2013)

Bottom Line: We derived the amplitude spectrum of three different frequency bands, theta, alpha, and beta of EEG.Then, based on fusion of discrete wavelet packet transforms and fast fourier transform yield, we extracted two features (power spectral density, spectral centroid frequency) of different wavelets (db4, db8, sym8, and coif5).A fuzzy inference system classifier was applied to different wavelets using the two extracted features. [Results] The results indicate that the two features of sym8 posses highly significant discrimination across the four levels of distraction, and the best average accuracy achieved by the subtractive fuzzy classifier was 79.21% using the power spectral density feature extracted using the sym8 wavelet. [Conclusion] These findings suggest that EEG signals can be used to monitor distraction level intensity in order to alert drivers to high levels of distraction.

View Article: PubMed Central - PubMed

Affiliation: School of Computer and Communication Engineering, University Malaysia Perlis.

ABSTRACT
[Purpose] In earlier studies of driver distraction, researchers classified distraction into two levels (not distracted, and distracted). This study classified four levels of distraction (neutral, low, medium, high). [Subjects and Methods] Fifty Asian subjects (n=50, 43 males, 7 females), age range 20-35 years, who were free from any disease, participated in this study. Wireless EEG signals were recorded by 14 electrodes during four types of distraction stimuli (Global Position Systems (GPS), music player, short message service (SMS), and mental tasks). We derived the amplitude spectrum of three different frequency bands, theta, alpha, and beta of EEG. Then, based on fusion of discrete wavelet packet transforms and fast fourier transform yield, we extracted two features (power spectral density, spectral centroid frequency) of different wavelets (db4, db8, sym8, and coif5). Mean ± SD was calculated and analysis of variance (ANOVA) was performed. A fuzzy inference system classifier was applied to different wavelets using the two extracted features. [Results] The results indicate that the two features of sym8 posses highly significant discrimination across the four levels of distraction, and the best average accuracy achieved by the subtractive fuzzy classifier was 79.21% using the power spectral density feature extracted using the sym8 wavelet. [Conclusion] These findings suggest that EEG signals can be used to monitor distraction level intensity in order to alert drivers to high levels of distraction.

No MeSH data available.


Related in: MedlinePlus

Five level EEG signal decomposition using Discrete Wavelet Packet Transform (DWPT)
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fig_001: Five level EEG signal decomposition using Discrete Wavelet Packet Transform (DWPT)

Mentions: In this work, the spectral features of the EEG signals of the different distraction levelswere derived for three EEG frequency bands, namely, theta, alpha and beta, by applying fourdifferent wavelets (db4, db8, sym8, and coif5). The waveforms of these wavelets are similarto waveforms in the EEG signal. We used discrete wavelet packet transforms (DWPT) forefficient frequency band localization. DWPT decomposes both the high and low frequencycomponents of the input signal into any level of decomposition unlike normal wavelettransforms which decompose only the approximation coefficients in the subsequent levels. Inthis work, DWPT was used to process three frequency bands, namely theta (4–8Hz), alpha(8–12Hz), and beta (14–32Hz) frequency bands to identify distraction levels as shown inFig. 1.


Subtractive fuzzy classifier based driver distraction levels classification using EEG.

Wali MK, Murugappan M, Ahmad B - J Phys Ther Sci (2013)

Five level EEG signal decomposition using Discrete Wavelet Packet Transform (DWPT)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3818778&req=5

fig_001: Five level EEG signal decomposition using Discrete Wavelet Packet Transform (DWPT)
Mentions: In this work, the spectral features of the EEG signals of the different distraction levelswere derived for three EEG frequency bands, namely, theta, alpha and beta, by applying fourdifferent wavelets (db4, db8, sym8, and coif5). The waveforms of these wavelets are similarto waveforms in the EEG signal. We used discrete wavelet packet transforms (DWPT) forefficient frequency band localization. DWPT decomposes both the high and low frequencycomponents of the input signal into any level of decomposition unlike normal wavelettransforms which decompose only the approximation coefficients in the subsequent levels. Inthis work, DWPT was used to process three frequency bands, namely theta (4–8Hz), alpha(8–12Hz), and beta (14–32Hz) frequency bands to identify distraction levels as shown inFig. 1.

Bottom Line: We derived the amplitude spectrum of three different frequency bands, theta, alpha, and beta of EEG.Then, based on fusion of discrete wavelet packet transforms and fast fourier transform yield, we extracted two features (power spectral density, spectral centroid frequency) of different wavelets (db4, db8, sym8, and coif5).A fuzzy inference system classifier was applied to different wavelets using the two extracted features. [Results] The results indicate that the two features of sym8 posses highly significant discrimination across the four levels of distraction, and the best average accuracy achieved by the subtractive fuzzy classifier was 79.21% using the power spectral density feature extracted using the sym8 wavelet. [Conclusion] These findings suggest that EEG signals can be used to monitor distraction level intensity in order to alert drivers to high levels of distraction.

View Article: PubMed Central - PubMed

Affiliation: School of Computer and Communication Engineering, University Malaysia Perlis.

ABSTRACT
[Purpose] In earlier studies of driver distraction, researchers classified distraction into two levels (not distracted, and distracted). This study classified four levels of distraction (neutral, low, medium, high). [Subjects and Methods] Fifty Asian subjects (n=50, 43 males, 7 females), age range 20-35 years, who were free from any disease, participated in this study. Wireless EEG signals were recorded by 14 electrodes during four types of distraction stimuli (Global Position Systems (GPS), music player, short message service (SMS), and mental tasks). We derived the amplitude spectrum of three different frequency bands, theta, alpha, and beta of EEG. Then, based on fusion of discrete wavelet packet transforms and fast fourier transform yield, we extracted two features (power spectral density, spectral centroid frequency) of different wavelets (db4, db8, sym8, and coif5). Mean ± SD was calculated and analysis of variance (ANOVA) was performed. A fuzzy inference system classifier was applied to different wavelets using the two extracted features. [Results] The results indicate that the two features of sym8 posses highly significant discrimination across the four levels of distraction, and the best average accuracy achieved by the subtractive fuzzy classifier was 79.21% using the power spectral density feature extracted using the sym8 wavelet. [Conclusion] These findings suggest that EEG signals can be used to monitor distraction level intensity in order to alert drivers to high levels of distraction.

No MeSH data available.


Related in: MedlinePlus