Fatigue driving is one of the leading causes of traffic accidents, posing a significant threat to drivers and road safety. Most existing methods focus on studying whole-brain multi-channel electroencephalogram (EEG) signals, which involve a large number of channels, complex data processing, and cumbersome wearable devices. To address this issue, this paper proposes a fatigue detection method based on frontal EEG signals and constructs a fatigue driving detection model using an asymptotic hierarchical fusion network. The model employed a hierarchical fusion strategy, integrating an attention mechanism module into the multi-level convolutional module. By utilizing both cross-attention and self-attention mechanisms, it effectively fused the hierarchical semantic features of power spectral density (PSD) and differential entropy (DE), enhancing the learning of feature dependencies and interactions. Experimental validation was conducted on the public SEED-VIG dataset. The proposed model achieved an accuracy of 89.80% using only four frontal EEG channels. Comparative experiments with existing methods demonstrate that the proposed model achieves high accuracy and superior practicality, providing valuable technical support for fatigue driving monitoring and prevention.
疲劳驾驶是造成交通事故的主要原因之一，给驾驶员和道路安全构成了严重的威胁。当前的方法大多数都是对于全脑多通道的脑电信号进行研究，通道数较多，数据处理复杂且穿戴设备繁琐。为了解决这一问题，本文提出了基于前额脑电通道信号的疲劳检测方法，构建了基于渐近层次融合网络的疲劳驾驶检测模型。采用渐近层次融合策略，在多层次卷积模块中引入注意力机制模块，利用交叉注意力机制和自注意力机制融合功率谱密度（PSD）和微分熵特征（DE）的层次语义特征，更好地学习了特征之间的依赖关系和交互关系，提高了模型的鲁棒性和泛化能力。基于SEED-VIG公开数据集对该模型进行了实验验证，在仅采用前额4个脑电通道数据的情况下，取得了89.80%的准确率，并与其他现有方法进行了对比。实验结果表明本文提出的模型具有较高的准确性以及更强的实用性，可为疲劳驾驶监测和预防提供重要的技术支持。.
Keywords: Asymptotic hierarchical fusion; Convolutional neural network; Cross-attention mechanism; Fatigue driving detection; Frontal electroencephalogram.