Early discernment of drivers drowsy state may prevent numerous worldwide road accidents. Electroencephalogram (EEG) signals provide valuable information about the neurological changes for discrimination of alert and drowsy state. A signal is decomposed into multi-components for the analysis of the physiological state. Tunable Q wavelet transform (TQWT) decomposes the signal into low-pass and high-pass sub-bands without a choice of wavelet. The information content captured by these sub-bands depends on the choice of decomposition parameters. Due to the non-stationary nature of EEG signals, the predefined decomposition parameters of TQWT lead to information loss and degrade system performance. Hence it is required to automate the decomposition parameters in accordance with the nature of signals. In this paper, an optimized tunable Q wavelet transform (O-TQWT) is proposed for the adaptive selection of decomposition parameters by using different optimization algorithms. Objective function as a mean square error (MSE) of decomposition is minimized by optimization algorithms. Optimum decomposition parameters are used to decompose the signals into sub-bands. Time-domain based features are excerpted from the sub-bands of O-TQWT. Highly discriminant features selected by using Kruskal Wallis test are used as an input to different classification techniques. Classification accuracy of 96.14% is achieved by least square support vector machine with radial basis function kernel which is better than the other existing methodologies using the same database.

及早识别驾驶员的昏昏欲睡状态可能会预防世界范围内的众多道路事故。脑电图 (EEG) 信号为区分警觉和困倦状态提供了有关神经系统变化的有价值信息。一个信号被分解成多分量用于生理状态的分析。可调Q小波变换 (TQWT) 将信号分解为低通和高通子带，无需选择小波。这些子带捕获的信息内容取决于分解参数的选择。由于EEG信号的非平稳性，TQWT的预定义分解参数会导致信息丢失并降低系统性能。因此需要根据信号的性质使分解参数自动化。在本文中，优化的可调Q针对分解参数的自适应选择，提出了小波变换（O-TQWT），采用不同的优化算法。作为分解的均方误差 (MSE) 的目标函数通过优化算法最小化。最佳分解参数用于将信号分解成子带。基于时域的特征摘自 O-TQWT 的子带。通过使用 Kruskal Wallis 测试选择的高度判别特征被用作不同分类技术的输入。具有径向基函数内核的最小二乘支持向量机的分类精度达到了 96.14%，优于使用相同数据库的其他现有方法。