Abstract：
OBJECTIVE: Electroencephalography (EEG) is widely recognized as an effective method for detecting fatigue. However, practical applications of EEG for fatigue detection in real-world scenarios are often challenging, particularly in cases involving subjects not included in the training datasets, owing to bio-individual differences and noisy labels. This study aims to develop an effective framework for cross-subject fatigue detection by addressing these challenges.
METHODS: In this study, we propose a novel framework, termed DP-MP, for cross-subject fatigue detection, which utilizes a Domain-Adversarial Neural Network (DANN)-based prototypical representation in conjunction with Mix-up pairwise learning. Our proposed DP-MP framework aims to mitigate the impact of bio-individual differences by encoding fatigue-related semantic structures within EEG signals and exploring shared fatigue prototype features across individuals. Notably, to the best of our knowledge, this work is the first to conceptualize fatigue detection as a pairwise learning task, thereby effectively reducing the interference from noisy labels. Furthermore, we propose the Mix-up pairwise learning (MixPa) approach in the field of fatigue detection, which broadens the advantages of pairwise learning by introducing more diverse and informative relationships among samples.
RESULTS: Cross-subject experiments were conducted on two benchmark databases, SEED-VIG and FTEF, achieving state-of-the-art performance with average accuracies of 88.14% and 97.41%, respectively. These promising results demonstrate our model\'s effectiveness and excellent generalization capability.
CONCLUSIONS: This is the first time EEG-based fatigue detection has been conceptualized as a pairwise learning task, offering a novel perspective to this field. Moreover, our proposed DP-MP framework effectively tackles the challenges of bio-individual differences and noisy labels in the fatigue detection field and demonstrates superior performance. Our work provides valuable insights for future research, promoting the application of brain-computer interfaces for fatigue detection in real-world scenarios. .
METHODS: In this study, we propose a novel framework, termed DP-MP, for cross-subject fatigue detection, which utilizes a Domain-Adversarial Neural Network (DANN)-based prototypical representation in conjunction with Mix-up pairwise learning. Our proposed DP-MP framework aims to mitigate the impact of bio-individual differences by encoding fatigue-related semantic structures within EEG signals and exploring shared fatigue prototype features across individuals. Notably, to the best of our knowledge, this work is the first to conceptualize fatigue detection as a pairwise learning task, thereby effectively reducing the interference from noisy labels. Furthermore, we propose the Mix-up pairwise learning (MixPa) approach in the field of fatigue detection, which broadens the advantages of pairwise learning by introducing more diverse and informative relationships among samples.
RESULTS: Cross-subject experiments were conducted on two benchmark databases, SEED-VIG and FTEF, achieving state-of-the-art performance with average accuracies of 88.14% and 97.41%, respectively. These promising results demonstrate our model\'s effectiveness and excellent generalization capability.
CONCLUSIONS: This is the first time EEG-based fatigue detection has been conceptualized as a pairwise learning task, offering a novel perspective to this field. Moreover, our proposed DP-MP framework effectively tackles the challenges of bio-individual differences and noisy labels in the fatigue detection field and demonstrates superior performance. Our work provides valuable insights for future research, promoting the application of brain-computer interfaces for fatigue detection in real-world scenarios. .
摘要：
目的：脑电图（EEG）被广泛认为是检测疲劳的有效方法。然而,EEG在现实世界场景中用于疲劳检测的实际应用通常具有挑战性，特别是在涉及未包含在训练数据集中的受试者的情况下，由于生物个体差异和嘈杂的标签。本研究旨在通过解决这些挑战，为跨学科疲劳检测开发一个有效的框架。
方法：在本研究中，我们提出了一个新的框架，称为DP-MP，用于跨主题疲劳检测，它利用基于领域对抗神经网络（DANN）的原型表示与混合成对学习相结合。我们提出的DP-MP框架旨在通过在EEG信号中编码与疲劳相关的语义结构并探索跨个体的共享疲劳原型特征来减轻生物个体差异的影响。值得注意的是,据我们所知,这项工作是第一个概念化疲劳检测作为一个成对学习任务，从而有效地减少来自噪声标签的干扰。此外，我们在疲劳检测领域提出了混合成对学习(MixPa)方法，通过在样本之间引入更多样化和信息丰富的关系，拓宽了成对学习的优势。
结果：在两个基准数据库上进行了跨学科实验，SEED-VIG和FTEF，实现最先进的性能，平均精度为88.14%和97.41%，分别。这些有希望的结果证明了我们模型的有效性和出色的泛化能力。
结论：这是首次将基于EEG的疲劳检测概念化为成对学习任务，为这一领域提供了新的视角。此外，我们提出的DP-MP框架有效地解决了疲劳检测领域中生物个体差异和嘈杂标签的挑战，并展示了卓越的性能。我们的工作为未来的研究提供了宝贵的见解，促进脑机接口在现实场景中的疲劳检测应用。 .
方法：在本研究中，我们提出了一个新的框架，称为DP-MP，用于跨主题疲劳检测，它利用基于领域对抗神经网络（DANN）的原型表示与混合成对学习相结合。我们提出的DP-MP框架旨在通过在EEG信号中编码与疲劳相关的语义结构并探索跨个体的共享疲劳原型特征来减轻生物个体差异的影响。值得注意的是,据我们所知,这项工作是第一个概念化疲劳检测作为一个成对学习任务，从而有效地减少来自噪声标签的干扰。此外，我们在疲劳检测领域提出了混合成对学习(MixPa)方法，通过在样本之间引入更多样化和信息丰富的关系，拓宽了成对学习的优势。
结果：在两个基准数据库上进行了跨学科实验，SEED-VIG和FTEF，实现最先进的性能，平均精度为88.14%和97.41%，分别。这些有希望的结果证明了我们模型的有效性和出色的泛化能力。
结论：这是首次将基于EEG的疲劳检测概念化为成对学习任务，为这一领域提供了新的视角。此外，我们提出的DP-MP框架有效地解决了疲劳检测领域中生物个体差异和嘈杂标签的挑战，并展示了卓越的性能。我们的工作为未来的研究提供了宝贵的见解，促进脑机接口在现实场景中的疲劳检测应用。 .
