疲勞駕駛是車輛發生事故的一個主要原因；眨眼的周期變化計算是疲勞駕駛可以判斷的主要因素之一。腦內神經的真實狀況不易由外表知曉，但腦內神經所傳達的訊息是無法掩飾而能真正顯示駕駛者現在的疲勞狀況，因此只要能夠找到方法測出腦內神經訊息，即可用來分析駕駛者的疲勞狀態。評估出的結果與事實具有相當的穩定性及正確性。本文主要採用頻譜分析方法，並使用有限脈衝響應數位濾波器，因其具有高精確度和沒有漂移，可以容易的區分不同頻段。使用微控制器將腦電圖（EEG）信號透過通用串行介面（USB）傳送到個人電腦，經過有200個閥門(taps)的數位濾波器程序，可以分析出單獨信號的頻段。本論文調查了四個腦電圖頻段δ，θ，α和β，採用四種演算法去評估疲勞的狀態。我們比較四種演算法並找出最好的一個。
本論文提出一種方法來檢測眼睛眨眼的週期變化狀況，類似於眼電圖（EOG）的表現方式，但我們的檢測系統中只採用兩個電極貼片，感測元件為簡單的電極貼片做為輸入端，而獲得正確的眼皮肌電訊號，可去除偽信號(非肌電訊號)。經由微控制器即時計算並顯示出眨眼的週期長短及眨眼的次數，然後再進行疲勞的判斷。根據測量，所計算眨眼的次數和真實狀況相當吻合；而眨眼週期時間的準確度計算超過95％。

Driver fatigue is a major cause of vehicle accidents. The computation of eye-blink durations is one of the main schemes to warn of driver fatigue. From outside, the condition of cranial nerves is concealed in the driver's brain. But they convey the message that can really explain the current fatigue condition. Therefore, if the real nerve signals can be measured, the fatigue states identification can achieve high stability and accuracy. Spectral analysis is the major method to identify the fatigue states. Various frequency bands can be distinguished by finite impulse filters (FIR) due to their high accuracy and drift-less features. The electro-encephalogram (EEG) signal is sent to a personal computer via Universal Serial Bus (USB) interface from microcontroller and passed through the digital filters procedure with 200 taps, and thus the spectrum of individual signals can be analyzed. This study has investigated the four EEG frequency bands, delta (δ), theta (θ), alpha (α) and beta (β), using four equations to evaluate fatigue state based on the EEG signals. We made comparisons between the four equations, and searched out the best one.
This thesis proposes a methodology to detect eye-blink durations, which is similar to EOG (electro-oculogram or electro-oculography), but only two electrode pads are employed in our detection system. Simple electrode pads are used as sensors to obtain the right eyelid EOG signal and remove the artificial pseudo-signal (non-EOG). A real-time microcontroller calculates and shows the blink-durations and number of blinks. The measurement results are analyzed to warn of driver fatigue. According to our measurements, the calculations of the numbers of blinks and the real condition match pretty well. The accuracy of blink duration determination is close to 95%.

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