The existed methods for electroencephalograph (EEG) emotion recognition always train the models based on all the EEG samples indistinguishably. However, some of the source (training) samples may lead to a negative influence because they are significant dissimilar with the target (test) samples. So it is necessary to give more attention to the EEG samples with strong transferability rather than forcefully training a classification model by all the samples. Furthermore, for an EEG sample, from the aspect of neuroscience, not all the brain regions of an EEG sample contains emotional information that can transferred to the test data effectively. Even some brain region data will make strong negative effect for learning the emotional classification model. Considering these two issues, in this paper, we propose a transferable attention neural network (TANN) for EEG emotion recognition, which learns the emotional discriminative information by highlighting the transferable EEG brain regions data and samples adaptively through local and global attention mechanism. This can be implemented by measuring the outputs of multiple brain-region-level discriminators and one single sample-level discriminator. We conduct the extensive experiments on three public EEG emotional datasets. The results validate that the proposed model achieves the state-of-the-art performance.

中文翻译：

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一种用于 EEG 情绪识别的新型可转移注意力神经网络模型

现有的脑电图 (EEG) 情绪识别方法总是基于所有 EEG 样本无法区分地训练模型。然而，一些源（训练）样本可能会导致负面影响，因为它们与目标（测试）样本显着不同。因此需要更多地关注迁移性强的脑电样本，而不是强行通过所有样本训练一个分类模型。此外，对于脑电样本，从神经科学的角度来看，并非脑电样本的所有大脑区域都包含可以有效传递到测试数据的情绪信息。甚至一些大脑区域数据也会对学习情绪分类模型产生强烈的负面影响。考虑到这两个问题，在本文中，我们提出了一种用于 EEG 情绪识别的可转移注意力神经网络 (TANN)，它通过局部和全局注意力机制自适应地突出可转移 EEG 大脑区域数据和样本来学习情绪判别信息。这可以通过测量多个大脑区域级别鉴别器和一个单个样本级别鉴别器的输出来实现。我们对三个公共 EEG 情绪数据集进行了广泛的实验。结果验证了所提出的模型达到了最先进的性能。这可以通过测量多个大脑区域级鉴别器和一个单个样本级鉴别器的输出来实现。我们对三个公共 EEG 情绪数据集进行了广泛的实验。结果验证了所提出的模型达到了最先进的性能。这可以通过测量多个大脑区域级鉴别器和一个单个样本级鉴别器的输出来实现。我们对三个公共 EEG 情绪数据集进行了广泛的实验。结果验证了所提出的模型达到了最先进的性能。