本研究利用了θ的显著差异,α,和β波段功率谱在脑电图(EEG)中观察到的注意力分散和集中驾驶。三个子任务,视觉分心,听觉分心,和认知分心,被设计为在驾驶模拟过程中随机出现。θ,α,提取了四种驾驶注意状态的脑电信号的β波段功率谱,和SVM,EEGNet,和GRU-EEGNet模型用于检测驾驶注意力状态,分别。进行了在线实验。θ的提取,α,发现EEG信号的β波段功率谱特征比提取整个EEG信号的功率谱特征更有效地检测驾驶注意力状态。提出的GRU-EEGNet模型的驾驶注意力状态检测精度比EEGNet模型和PSD_SVM方法提高了6.3%和12.8%,分别。结合脑电特征和改进的深度学习算法的脑电解码方法,有效提高了驾驶注意力状态检测的准确性,是根据现有研究的结果手动初步选择的。
The present study utilizes the significant differences in θ, α, and β band power spectra observed in electroencephalograms (EEGs) during distracted versus focused driving. Three subtasks, visual distraction, auditory distraction, and cognitive distraction, were designed to appear randomly during driving simulations. The θ, α, and β band power spectra of the EEG signals of the four driving attention states were extracted, and
SVM, EEGNet, and GRU-EEGNet models were employed for the detection of the driving attention states, respectively. Online experiments were conducted. The extraction of the θ, α, and β band power spectrum features of the EEG signals was found to be a more effective method than the extraction of the power spectrum features of the whole EEG signals for the detection of driving attention states. The driving attention state detection accuracy of the proposed GRU-EEGNet model is improved by 6.3% and 12.8% over the EEGNet model and PSD_SVM method, respectively. The EEG decoding method combining EEG features and an improved deep learning algorithm, which effectively improves the driving attention state detection accuracy, was manually and preliminarily selected based on the results of existing studies.