Tsai Pai-Yuan, Hu Weichih, Kuo Terry B J, Shyu Liang-Yu
Chung Yuan Christian University, Chung Li, ROC.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:3775-8. doi: 10.1109/IEMBS.2009.5334491.
Electroencephalogram (EEG) signals give important information about the vigilance states of a subject. Therefore, this study constructs a real-time EEG-based system for detecting a drowsy driver. The proposed system uses a novel six channels active dry electrode system to acquire EEG non-invasively. In addition, it uses a TMS320VC5510 DSP chip as the algorithm processor, and a MSP430F149 chip as a controller to achieve a real-time portable system. This study implements stationary wavelet transform to extract two features of EEG signal: integral of EEG and zero crossings as the input to a back propagation neural network for vigilance states classification. This system can discriminate alertness and drowsiness in real-time. The accuracy of the system is 79.1% for alertness and 90.91% for drowsiness states. When the system detects drowsiness, it will warn drivers by using a vibrator and a beeper.
脑电图（EEG）信号提供了有关受试者警觉状态的重要信息。因此，本研究构建了一个基于脑电图的实时系统，用于检测困倦驾驶的司机。所提出的系统使用一种新型的六通道有源干式电极系统以非侵入方式采集脑电图。此外，它使用TMS320VC5510 DSP芯片作为算法处理器，以及MSP430F149芯片作为控制器，以实现一个实时便携式系统。本研究实施平稳小波变换来提取脑电图信号的两个特征：脑电图积分和过零率，作为输入到反向传播神经网络中用于警觉状态分类。该系统可以实时区分警觉和困倦状态。该系统对警觉状态的准确率为79.1%，对困倦状态的准确率为90.91%。当系统检测到困倦时，它将使用振动器和蜂鸣器警告司机。
