A cross-attention swin transformer network for EEG-based subject-independent cognitive load assessment

IF 3.1 3区 工程技术 Q2 NEUROSCIENCES
Zhongrui Li, Rongkai Zhang, Li Tong, Ying Zeng, Yuanlong Gao, Kai Yang, Bin Yan
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Abstract

EEG signals play a crucial role in assessing cognitive load, which is a key element in ensuring the secure operation of human–computer interaction systems. However, the variability of EEG signals across different subjects poses a challenge in applying the pre-trained cognitive load assessment model to new subjects. Moreover, previous domain adaptation research has primarily focused on developing complex network architectures to learn more domain-invariant features, overlooking the noise introduced by pseudo-labels and the challenges posed by domain migration problems. Therefore, this study proposes a novel cross-attention swin-transformer network for cross-subject cognitive load assessment, which achieves inter-domain feature alignment through parameter sharing in cross attention mechanism without using pseudo-labels, and utilizes maximum mean discrepancy (MMD) to measure the difference between the feature distributions of the source and target domains, further promoting feature alignment between domains. This method aims to leverage the advantages of cross-attention mechanism and MMD to better mitigate individual differences among subjects in cross-subject cognitive workload assessment. To validate the classification performance of the proposed network, two datasets of image recognition task and N-back task were employed for testing. Results show that, the proposed model outperformed advanced methods with cross-subject classification results of 88.13% and 81.27% on the on local and public datasets. The ablation experiment results reveal that using either the cross-attention mechanism or the MMD strategy alone improves cross-subject classification performance by 2.11% and 2.95% on the local dataset, respectively. Furthermore, the results of the EEG features distribution differences between all subjects before and after network training showed a significant reduction in feature distribution differences between subjects, further confirming the network’s effectiveness in minimizing inter-subject differences.

Abstract Image

基于脑电图的受试者独立认知负荷评估的交叉注意斯温变压器网络
脑电信号在评估认知负荷方面起着至关重要的作用,而认知负荷是确保人机交互系统安全运行的关键因素。然而,不同受试者的脑电信号存在差异,这给将预先训练好的认知负荷评估模型应用于新受试者带来了挑战。此外,以往的领域适应研究主要集中在开发复杂的网络架构,以学习更多的领域不变特征,忽略了伪标签带来的噪声和领域迁移问题带来的挑战。因此,本研究提出了一种用于跨主体认知负荷评估的新型交叉注意swin-transformer网络,通过交叉注意机制中的参数共享实现域间特征对齐,而不使用伪标签,并利用最大均值差异(MMD)测量源域和目标域特征分布的差异,进一步促进域间特征对齐。该方法旨在利用交叉注意机制和最大均值差异的优势,在跨受试者认知工作量评估中更好地减轻受试者之间的个体差异。为了验证所提网络的分类性能,我们使用了图像识别任务和 N-back 任务两个数据集进行测试。结果表明,在本地数据集和公共数据集上,所提模型的跨主体分类结果分别为 88.13% 和 81.27%,优于先进方法。消融实验结果显示,在本地数据集上,单独使用交叉注意机制或 MMD 策略可将跨主体分类性能分别提高 2.11% 和 2.95%。此外,所有受试者在网络训练前后的脑电图特征分布差异结果显示,受试者之间的特征分布差异显著减少,进一步证实了网络在最小化受试者间差异方面的有效性。
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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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