1.1 Motivation and Problems
Driver’s fatigue is a causal factor in many accidents because of the marked decline in the driver’s abilities of perception, recognition, and vehicle control abilities while sleepy. Hence, the development of the drowsiness monitoring technology to prevent accidents behind the steering wheel has received increasing interest in the field of automotive safety. Lately, several studies have shown that drowsiness related information is available in eye closures, heart rate and electroencephalogram (EEG) Previous studies showed that the eye blink duration and the blink rate typically increase while blink amplitude decreases as a function of the cumulative time, and the saccade frequencies and velocities of electrooculogram (EOG) decline when people get drowsy [25]. Although these studies showed that eye-activity variations were highly correlated with the human fatigue and could accurately and quantitatively estimate alertness levels, the step size (temporal resolution) of these eye-activity based methods was too slow (10s or longer) to track momentary changes in vigilance.
Contrarily, the temporal resolution of EEG-based methods could reach 1-2 sec that makes them faster enough to track second-to-second fluctuations in the subject’s performance. Although EEG signals have been proved to index the cognitive states of a person, signal analysis is very challenge in EEG-based systems because of the pervasive contaminations from eye movements, blinks, muscle, heart, and line noise to the EEG. Independent component analysis (ICA) has been proved to be an effective technique to remove various types of artifacts [23][24]. However, most of the ICA in these studies was performed offline on personal computers instead of an online system. For eventual practical acceptance in the workplace, it is highly desirable to make all data acquisition and analysis on-lined. Here, we report our work in the
design and test of a wireless embedded brain computer interface (BCI) that comprises three functional modules: (1) EEG acquisition, amplification and wireless transmission; (2) on-line ICA process, and (3) real-time drowsiness detection to accurately and continuously detect subject drowsiness level based on the EEG data and feedback delivery.
1.2 Organization of Thesis
The organization of this thesis goes as follows:
z Chapter 2
In chapter 2 we briefly describe the history of BCI, ICA and other drowsiness detection methods. Three kinds of drowsiness detection methods will be described here.
z Chapter 3
In chapter 3 we briefly describe the basic architecture of BCI. Then we will propose a new embedded BCI system that can assess subject drowsiness levels in near real time. In this chapter we will address the following issues:
Hardware specification: The hardware specification of the embedded BCI system will be described here. Hardware specification includes an EEG recording, amplification and wireless transmitting unit and an embedded digital signal processing (DSP) board.
The operating system (OS): The OS for the embedded DSP board will be described here.
The applications implemented on the embedded digital signal processor: The applications on the signal processor include an on-line ICA process, spectral estimation and a real-time drowsiness detection
algorithm. The techniques used to verify the application will be described here as well.
z Chapter 4
In chapter 4 we briefly describe the theory of ICA and the technique for improving the convergence of ICA algorithm.
z Chapter 5
In chapter 5 we describe the experimental design. The experiments are designed to simulate highway driving during which drivers often struggle to maintain their alertness and attention. The correspondence between a driver’s behavioral and estimated performance obtained by the embedded DSP will be shown and discussed here.
z Chapter 6
We will conclude our work in Chapter 6.
1.3 Notation
Abbreviation Original text Chinese Translation
A/D Analog to Digital 類比數位訊轉換
API Application Program Interface 應用程式介面
BCI Brain Computer Interface 大腦人機介面
DMA Direct Memory Access 直接記憶體存取
DSP Digital Signal Processor 數位訊號處理器
ECG Electrocardiogram 心電圖
EEG Electroencephalogram 腦電波
EMG Electromyogram 肌電圖
EOG Electro-oculogram 眼電圖
FHSS Frequency Hopping Spread Spectrum 跳頻展頻
FPGA Field Programmable Array 可程式化邏輯閘陣列
FSK Frequency Shift Keying 頻率鍵移
GCC GNU Compiler Collection GNU 編譯器
GSM
Global System for Mobile Communication
全球行動通訊系統
GPRS General Packet Radio Service 通用分組無線服務
IDE Integrated Development Environment 整合發展環境
IP Internet Protocol 網際網路通訊協定
ICA Independent Component Analysis 獨立成份分析
LCD Liquid Crystal Display 液晶平面顯示器
MMU Memory Management Unit 記憶體管理單元
OS Operating System 作業系統
PCA Principle component analysis 主成分分析
PDA Personal Digital Assistant 個人數位助理
RF Radio Frequency 射頻
SoC System on Chip 系統晶片
STFFT Short-Time FFT 短時快速傅立葉轉換
TCP Transmission Control Protocol 傳輸控制協定
TCS Telephone Control Service 電話傳送控制協定
UART
Universal Asynchronous Receiver Transmitter
通用異步收發器
USB Universal Serial Bus 通用串列匯流排