软件导刊2025，Vol.24Issue(3)：127-136,10.DOI:10.11907/rjdk.241023
一个用于疲劳驾驶检测的多尺度卷积神经网络
A Multiscale Convolutional Neural Network for Fatigue Driving Detection
摘要
Abstract
Assessments based on electroencephalographic(EEG)signals are considered to be one of the most predictive and reliable methods for driving fatigue research.However,research in this area still has a low classification power in cross-subject studies and there are no uniform standards for classification metrics,and there are no recognised and validated methods for fatigue detection.Many methods simply extract spa-tiotemporal features and process the data directly with cumbersome parameter adjustments,ignoring differences in EEG performance across subjects.In addition,the contribution of different EEG channels to driving fatigue detection is also ignored.In order to solve the above prob-lems,a multiscale convolutional neural network(MSCNN)based on the attention mechanism is proposed.The network includes a multiscale feature extraction layer,a feature recognition layer and a compressive classification layer.In the multiscale feature extraction layer,the net-work is able to automatically extract effective features from EEG signals.In the feature recognition layer,the effective features extracted in the previous layer are filtered and the data classification is finally completed by the compressed classification layer.Validated by using the public-ly available SEED-VIG and self-made Simulated Fatigue Driving(SFDE)datasets,the experimental results show that the classification accu-racies of the MSCNN on SEED-VIG and SFDE data for the mixed experiments are 91.36%and 92.06%,and the classification accuracies across the subjects are 75.54%and 76.52%,which are higher than those of the current state-of-the-art methods.In addition,by analysing the attentional activation weights and brain topology maps of the model,we investigate the contribution of different brain regions to the classifi-cation detection as well as the trend of different frequency bands between the two states.In conclusion,this study provides new research ideas for the application of brain-computer interfaces in driving fatigue research and promotes the further development of this field towards practical applications.关键词
脑电信息/疲劳驾驶检测/注意力机制/多尺度Key words
EEG/fatigue driving detection/attention mechanism/multiscale分类
信息技术与安全科学引用本文复制引用
陆全平,曾帅,李芃锐,郜东瑞,陈俊..一个用于疲劳驾驶检测的多尺度卷积神经网络[J].软件导刊,2025,24(3):127-136,10.基金项目
国家自然科学青年基金项目(82102175) （82102175）
