计算机工程2025，Vol.51Issue(10)：87-96,10.DOI:10.19678/j.issn.1000-3428.0069857
基于注意力机制的神经网络优化模型的行驶疲劳度研究
Study of Driving Fatigue Level Using Optimized Neural Network Models Based on Attention Mechanisms
李博文 1丁牧恒 1方美华 1朱桂平 1魏志勇 1成巍 2李亚云 2卞双双2
作者信息
- 1. 南京航空航天大学航天学院,江苏南京 211100
- 2. 智慧地球重点实验室,北京 100094
- 折叠
摘要
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.关键词
脑电图/疲劳度/特征/注意力机制/神经网络模型Key words
Electroencephalogram(EEG)/fatigue level/feature/attention mechanism/neural network model分类
计算机与自动化引用本文复制引用
李博文,丁牧恒,方美华,朱桂平,魏志勇,成巍,李亚云,卞双双..基于注意力机制的神经网络优化模型的行驶疲劳度研究[J].计算机工程,2025,51(10):87-96,10.
