Abstract:

Driver fatigue is a major cause of traffic accidents, and driver fatigue state classification based on Electroencephalograms (EEGs) is an important task in the field of artificial intelligence. In recent years, deep learning models that incorporate attention mechanisms have been widely applied to EEG-based fatigue recognition. While these approaches have shown promise, several studies disregard the inherent features of EEG data itself. Additionally, the exploration of the mechanisms and effects of attention on the classifier is vague, which results in failure to explain the specific effects of different attention states on classification performance. Therefore, this study selects the SEED-VIG data as the research object and adopts the ReliefF feature selection algorithm to construct optimized models of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Support Vector Machine (SVM) based on self attention, multihead attention, channel attention, and spatial attention mechanisms. Experimental results on the EEG data included in the SEED-VIG dataset show that the performance of several neural network optimization models based on multimodal attention mechanisms has improved in terms of accuracy, recall rate, F1 score, and other indicators. Among them, the Convolutional Block Attention Module (CBAM)-CNN model, which can enhance spatial and channel information, achieves the best performance with 84.7% mean accuracy with 0.66 standard deviation.

Key words: Electroencephalogram (EEG), fatigue level, feature, attention mechanism, neural network model

摘要：

疲劳驾驶是导致交通事故的主要因素之一。在人工智能领域, 基于脑电图(EEG)的驾驶疲劳状态分类已成为重要研究方向。近年来, 融合注意力机制的深度学习模型在EEG疲劳识别中得到了广泛应用。以SEED-VIG数据集作为研究对象, 采用ReliefF特征选择算法, 构建基于自注意力、多头注意力、通道注意力、空间注意力机制的卷积神经网络(CNN)、长短期记忆(LSTM)网络和支持向量机(SVM)优化模型。在SEED-VIG数据集提供的EEG数据上的实验结果表明, 基于多模注意力机制的多种神经网络优化模型的准确率、召回率、F1值等指标均得到了有效提升, 其中以平均准确率和标准偏差作为对比参数, 可增强空间与通道信息的卷积块注意力模块(CBAM)-CNN模型的性能最佳, 分别为84.7%和0.66。

关键词: 脑电图, 疲劳度, 特征, 注意力机制, 神经网络模型