Li Song, Fu Yunfa, Zhang Yan, Lu Gong
Intelligent System Laboratory of Qinghai University, Xining 810000, P. R. China.
Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2025 Aug 25;42(4):686-692. doi: 10.7507/1001-5515.202410003.
Electroencephalogram (EEG) serves as an effective indicator of detecting fatigue driving. Utilizing the open accessible Shanghai Jiao Tong University Emotion Electroencephalography Dataset (SEED-VIG), driving states are divided into three categories including awake, tired and drowsy for investigation. Given the characteristics of mutual influence and interdependence among EEG channels, as well as the consistency of the graph convolutional neural network (GCNN) structure, we designed an adjacency matrix based on the Pearson correlation coefficients of EEG signals among channels and their positional relationships. Subsequently, we developed a GCNN for recognition. The experimental results show that the average classification accuracy of driving state categories for 20 subjects, from the SEED-VIG dataset under the smooth feature of differential entropy (DE) linear dynamic system is 91.66%. Moreover, the highest classification accuracy can reach 98.87%, and the average Kappa coefficient is 0.83. This work demonstrates the reliability of this method and provides a guideline for the research field of safe driving brain computer interface.
脑电图（EEG）是检测疲劳驾驶的有效指标。利用公开可用的上海交通大学情感脑电图数据集（SEED-VIG），将驾驶状态分为清醒、疲劳和困倦三类进行研究。考虑到脑电图通道之间相互影响和相互依存的特性，以及图卷积神经网络（GCNN）结构的一致性，我们基于通道间脑电图信号的皮尔逊相关系数及其位置关系设计了一个邻接矩阵。随后，我们开发了一种用于识别的GCNN。实验结果表明，在微分熵（DE）线性动态系统的平滑特征下，来自SEED-VIG数据集的20名受试者驾驶状态类别的平均分类准确率为91.66%。此外，最高分类准确率可达98.87%，平均卡帕系数为0.83。这项工作证明了该方法的可靠性，并为安全驾驶脑机接口研究领域提供了指导。
