基于注意力的跨频图卷积网络用于驾驶员疲劳估计

IF 3.1 3区 工程技术 Q2 NEUROSCIENCES
Jianpeng An, Qing Cai, Xinlin Sun, Mengyu Li, Chao Ma, Zhongke Gao
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引用次数: 0

摘要

疲劳驾驶是造成全球车辆事故和死亡的重要原因,因此对驾驶员疲劳程度的估计至关重要。脑电图(EEG)已被证明是一种可靠的大脑状态预测工具。随着深度学习(DL)技术的进步,大脑状态估计算法也得到了显著改善。然而,脑电图的多域性质和脑电图通道之间错综复杂的时空频率相关性给开发精确的深度学习模型带来了挑战。在这项工作中,我们介绍了一种创新的基于注意力的跨频图卷积网络(ACF-GCN),用于利用θ、α和β波段的脑电信号估计驾驶员的反应时间。该方法利用多头注意力机制来检测不同频率的脑电图通道之间的长程依赖关系。同时,变换器的编码器模块从注意力分数矩阵中学习节点级特征图。随后,图卷积网络(GCN)将该矩阵与特征图进行整合,以估计驾驶员的反应时间。我们在公开数据集上进行的验证表明,ACF-GCN 的性能优于几种最先进的方法。我们还探索了跨频注意力-分数矩阵中的大脑动态,发现θ和α波段是影响疲劳估计性能的关键因素。ACF-GCN 方法推进了大脑状态估计,并提供了对多通道脑电信号背后的大脑动态的深入了解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Attention-based cross-frequency graph convolutional network for driver fatigue estimation

Attention-based cross-frequency graph convolutional network for driver fatigue estimation

Fatigue driving significantly contributes to global vehicle accidents and fatalities, making driver fatigue level estimation crucial. Electroencephalography (EEG) is a proven reliable predictor of brain states. With Deep Learning (DL) advancements, brain state estimation algorithms have improved significantly. Nonetheless, EEG’s multi-domain nature and the intricate spatial-temporal-frequency correlations among EEG channels present challenges in developing precise DL models. In this work, we introduce an innovative Attention-based Cross-Frequency Graph Convolutional Network (ACF-GCN) for estimating drivers’ reaction times using EEG signals from theta, alpha, and beta bands. This method utilizes a multi-head attention mechanism to detect long-range dependencies between EEG channels across frequencies. Concurrently, the transformer’s encoder module learns node-level feature maps from the attention-score matrix. Subsequently, the Graph Convolutional Network (GCN) integrates this matrix with feature maps to estimate driver reaction time. Our validation on a publicly available dataset shows that ACF-GCN outperforms several state-of-the-art methods. We also explore the brain dynamics within the cross-frequency attention-score matrix, identifying theta and alpha bands as key influencers in fatigue estimating performance. The ACF-GCN method advances brain state estimation and provides insights into the brain dynamics underlying multi-channel EEG signals.

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来源期刊
Cognitive Neurodynamics
Cognitive Neurodynamics 医学-神经科学
CiteScore
6.90
自引率
18.90%
发文量
140
审稿时长
12 months
期刊介绍: Cognitive Neurodynamics provides a unique forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics, intelligent science and applications, bridging the gap between theory and application, without any preference for pure theoretical, experimental or computational models. The emphasis is to publish original models of cognitive neurodynamics, novel computational theories and experimental results. In particular, intelligent science inspired by cognitive neuroscience and neurodynamics is also very welcome. The scope of Cognitive Neurodynamics covers cognitive neuroscience, neural computation based on dynamics, computer science, intelligent science as well as their interdisciplinary applications in the natural and engineering sciences. Papers that are appropriate for non-specialist readers are encouraged. 1. There is no page limit for manuscripts submitted to Cognitive Neurodynamics. Research papers should clearly represent an important advance of especially broad interest to researchers and technologists in neuroscience, biophysics, BCI, neural computer and intelligent robotics. 2. Cognitive Neurodynamics also welcomes brief communications: short papers reporting results that are of genuinely broad interest but that for one reason and another do not make a sufficiently complete story to justify a full article publication. Brief Communications should consist of approximately four manuscript pages. 3. Cognitive Neurodynamics publishes review articles in which a specific field is reviewed through an exhaustive literature survey. There are no restrictions on the number of pages. Review articles are usually invited, but submitted reviews will also be considered.
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