Wali Mousa Kadhim, Murugappan Murugappan, Ahmad Badlishah
School of Computer and Communication Engineering, University Malaysia Perlis.
J Phys Ther Sci. 2013 Sep;25(9):1055-8. doi: 10.1589/jpts.25.1055. Epub 2013 Oct 20.
[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.
[目的] 在早期关于驾驶员注意力分散的研究中，研究人员将注意力分散分为两个级别（未分散和分散）。本研究将注意力分散分为四个级别（中性、低、中、高）。[对象与方法] 五十名亚洲受试者（n = 50，43名男性，7名女性），年龄范围20 - 35岁，无任何疾病，参与了本研究。在四种注意力分散刺激（全球定位系统（GPS）、音乐播放器、短信服务（SMS）和思维任务）期间，通过14个电极记录无线脑电图信号。我们推导了脑电图的三个不同频段（θ、α和β）的振幅谱。然后，基于离散小波包变换和快速傅里叶变换结果的融合，我们提取了不同小波（db4、db8、sym8和coif5）的两个特征（功率谱密度、谱质心频率）。计算均值±标准差并进行方差分析（ANOVA）。使用这两个提取的特征，将模糊推理系统分类器应用于不同的小波。[结果] 结果表明，sym8的两个特征在注意力分散的四个级别上具有高度显著的区分能力，使用sym8小波提取的功率谱密度特征，减法模糊分类器实现的最佳平均准确率为79.21%。[结论] 这些发现表明，脑电图信号可用于监测注意力分散程度，以便提醒驾驶员注意高度分散的情况。
