Study of the Eye's Image Processing for the Determination of Driver's Fatigue

Chou, Zhi-Wei; Chen, Chi‐Wei; Huang, Kuo‐Lin; Jen, Sun-Lon; Hsu, Chia‐Wei; Yeh, Yu-Sing

doi:10.1109/icbbe.2009.5163455

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…The varying of amplitude and frequency of the wave represent various brain states [6], which depends on external stimulation and internal mental states [7]. The most familiar classification uses EEG waveform frequency (eg, Alpha, Beta, Theta, and Delta) [8].…”

Section: B Eeg Frequency Bandsmentioning

confidence: 99%

A mobile driver safety system: Analysis of single-channel EEG on drowsiness detection

Lim

Chia

Chin

2014

2014 International Conference on Computational Science and Technology (ICCST)

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Recent studies reveal that driving without sufficient sleep would increase the risk of road traffic accident. With the aim to facilitate a safer driving experience, eye activity detection algorithm were studied actively. Though the use of wired multi-channel brain computer interface (BCI) to monitor driver's mental state has shown promising results, but the actual practicality were limited by its inconvenience. Consequently, we examined the effectiveness of a wireless and wearable singlechannel BCI in detecting driver's eye-states. Using the NeuroSky MindWave headset that entailed a single-electrode for prefrontal cortex, we observed an increment of low alpha activity during the transition from eyes-open to eyes-closed state. A monitoring system to keep drivers awake by means of alarm notifications is then implemented using adaptive percentage threshold algorithm for alarm-triggering purpose. Through simulation, our algorithm has demonstrated an EEG eye-states recognition system with: adequate detection rate of 31% per second, negligible false alarm rate of 0.5%, and minimum latency of 2 seconds.

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Section: B Eeg Frequency Bandsmentioning

confidence: 99%