HMS-TENet:基于脑电图和眼电图的分层多尺度拓扑增强网络,用于驾驶员警惕性估计

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引用次数: 0

摘要

驾驶警觉性估计是交通安全的一项重要任务。目前,脑电图(EEG)和脑电图(EOG)在警觉性估计方面取得了一些成果,但仍面临一些挑战:1) 传统的直接多模态融合方法可能面临信息冗余和数据维度不匹配的问题;2) 在多模态融合过程中捕捉关键的判别特征,同时不丢失每种模态的特定模式。为了解决上述问题,本文提出了一种分波段脑电图和眼动图特征融合的方法,既保留了脑电图不同波段的脑活动信息,又有效地整合了眼动图的相关信息。在此基础上,我们进一步提出了分层多尺度拓扑增强网络(HMS-TENet)。该网络首先引入了金字塔池化结构(PPS),从不同的判别角度捕捉上下文关系。然后,我们设计了用于自适应感知场选择的选择性卷积结构(SCS),这使我们能够在小尺寸特征中挖掘所需的判别信息。此外,我们还设计了一种拓扑自我感知注意力,以加强对脑电图通道间复杂拓扑关系的表征学习。最后,该模型的输出可以选择用于回归和分类任务,从而提供更高的灵活性和适应性。我们在 SEED-VIG 公共数据集上进行的主体内和跨主体实验证明了所提方法的稳健性、通用性和实用性。代码见 https://github.com/tangmeng28/HMS-TENet。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HMS-TENet: A hierarchical multi-scale topological enhanced network based on EEG and EOG for driver vigilance estimation
Driving vigilance estimation is an important task for traffic safety. Nowadays, electroencephalography (EEG) and electrooculography (EOG) have made some achievements in vigilance estimation, but there are still some challenges: 1) The traditional approachs with direct multimodal fusion may face the problems of information redundancy and data dimensionality mismatch; 2) Capture key discriminative features during multimodal fusion without losing specific patterns to each modality. In order to solve the above problems, this paper proposes a approach with fusion of EEG and EOG features in split bands, which not only preserves the information about brain activities in different bands of EEG, but also effectively integrates the relevant information of EOG. On this basis, we further propose a hierarchical multi-scale topological enhanced network (HMS-TENet). This network first introduces a pyramid pooling structure (PPS) to capture contextual relationships from different discriminative perspectives. And then we design a selective convolutional structure (SCS) for adaptive sense-field selection, which enables us to mine the desired discriminative information in small-size features. In addition, we design a topology self-aware attention to enhance the learning of representations of complex topological relationships among EEG channels. Finally, the output of the model can be selected for both regression and classification tasks, providing higher flexibility and adaptability. We demonstrate the robustness, generalizability, and utility of the proposed method based on intra-subject and cross-subject experiments on the SEED-VIG public dataset. Codes are available at https://github.com/tangmeng28/HMS-TENet.
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