【摘要】 为准确识别驾驶疲劳，提出基于小波特征和长短期记忆（LSTM）神经网络分类器的驾驶疲劳识别方法。在真实驾驶环境下采集了驾驶员非疲劳状态与驾驶疲劳状态的脑电信号，对脑电信号进行小波分解，计算4个小波系数的统计值、能量值和相对能量作为特征数据，用特征数据对LSTM神经网络进行分类训练与测试。试验结果表明，随着所构建特征数据的通道数量增多，LSTM神经网络的分类性能逐渐提高，特别是在14通道方案下，平均分类准确率约为96.1%。更多还原

【Abstract】 In order to recognize driving fatigue, this paper proposed a fatigue detection method based on wavelet characteristics and Long Short-Term Memory(LSTM) neural network classifier. Two kinds of EEG signals(non-fatigue and driving fatigue) were collected in the real driving environment. The EEG signals were decomposed by wavelet, and the statistical values, energy values and relative energy values of four wavelet coefficients were calculated as the characteristic data, which were used for classification training and test of the LSTM neural network. The results of experiment show that the classification performance of LSTM neural network gradually improves with the increase of the number of channels involved in constructing characteristic data. Especially, in the scheme of 14 channels, the average classification accuracy is about 96.1%.更多还原