Gong Ming, Zhong Wei, Ye Long, Zhang Qin
Key Laboratory of Media Audio and Video (Communication University of China), Ministry of Education, Beijing, China.
State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China.
Front Neurosci. 2024 Feb 14;18:1293962. doi: 10.3389/fnins.2024.1293962. eCollection 2024.
When constructing machine learning and deep neural networks, the domain shift problem on different subjects complicates the subject independent electroencephalography (EEG) emotion recognition. Most of the existing domain adaptation methods either treat all source domains as equivalent or train source-specific learners directly, misleading the network to acquire unreasonable transfer knowledge and thus resulting in negative transfer.
This paper incorporates the individual difference and group commonality of distinct domains and proposes a multi-source information-shared network (MISNet) to enhance the performance of subject independent EEG emotion recognition models. The network stability is enhanced by employing a two-stream training structure with loop iteration strategy to alleviate outlier sources confusing the model. Additionally, we design two auxiliary loss functions for aligning the marginal distributions of domain-specific and domain shared features, and then optimize the convergence process by constraining gradient penalty on these auxiliary loss functions. Furthermore, the pre-training strategy is also proposed to ensure that the initial mapping of shared encoder contains sufficient emotional information.
We evaluate the proposed MISNet to ascertain the impact of several hyper-parameters on the domain adaptation capability of network. The ablation experiments are conducted on two publically accessible datasets SEED and SEED-IV to assess the effectiveness of each loss function.
The experimental results demonstrate that by disentangling private and shared emotional characteristics from differential entropy features of EEG signals, the proposed MISNet can gain robust subject independent performance and strong domain adaptability.
在构建机器学习和深度神经网络时，不同主体上的域转移问题使独立于主体的脑电图（EEG）情感识别变得复杂。大多数现有的域适应方法要么将所有源域视为等价的，要么直接训练特定于源的学习器，这会误导网络获取不合理的迁移知识，从而导致负迁移。
本文结合了不同域的个体差异和群体共性，提出了一种多源信息共享网络（MISNet），以提高独立于主体的EEG情感识别模型的性能。通过采用具有循环迭代策略的双流训练结构来增强网络稳定性，以减轻异常源对模型的干扰。此外，我们设计了两个辅助损失函数，用于对齐特定域和域共享特征的边缘分布，然后通过对这些辅助损失函数施加梯度惩罚来优化收敛过程。此外，还提出了预训练策略，以确保共享编码器的初始映射包含足够的情感信息。
我们评估了所提出的MISNet，以确定几个超参数对网络域适应能力的影响。在两个公开可用的数据集SEED和SEED-IV上进行了消融实验，以评估每个损失函数的有效性。
实验结果表明，通过从EEG信号的微分熵特征中分离出私有和共享的情感特征，所提出的MISNet可以获得强大的独立于主体的性能和强大的域适应性。
