EEG emotion recognition plays a significant role in various mental health services. Deep learning-based methods perform excellently, but still suffer from interpretability. Although methods such as Gradient-weighted Class Activation Mapping(Grad-CAM) can cope with the above problem, their coarse granularity cannot accurately reveal the mechanism to promote emotional intelligence. In this paper, fine-grained interpretability is proposed, called Concat-aided Grad-CAM. Specifically, the multi-level feature mapping before the fully connected layer is concatenated to obtain the gradients of the target concept so that the discriminant information can be directly located in the high-precision area. Unlike coarse-grained interpretability methods applied in EEG emotion recognition, it can accurately highlight the EEG channels related to emotion rather than an obscure area. In addition, a systematic brain functional network is proposed to reveal the relationship between those channels and to further improve emotion recognition performance. The channels with greater contributions are connected, and those connections are learned by dynamic graph convolutional networks, while the others are independent to eliminate interference. Experiments on four EEG emotion recognition datasets manifest that Concat-aided Grad-CAM can be interpreted by the fine-grained. In addition, it has been shown that the learned brain functional network can improve the performance of the baselines. Significantly, the experiment results achieve state-of-the-art performance in multiple experiments.

中文翻译：

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脑电图情绪识别的细粒度可解释性：Concat-Aided Grad-CAM 和系统大脑功能网络


脑电图情绪识别在各种心理健康服务中发挥着重要作用。基于深度学习的方法表现出色，但仍存在可解释性问题。虽然梯度加权类激活映射（Grad-CAM）等方法可以解决上述问题，但其粗粒度无法准确揭示提升情商的机制。在本文中，提出了细粒度的可解释性，称为 Concat-aided Grad-CAM。具体来说，将全连接层之前的多级特征映射进行级联，得到目标概念的梯度，从而可以将判别信息直接定位在高精度区域。与脑电情感识别中应用的粗粒度可解释性方法不同，它可以准确地突出与情感相关的脑电通道而不是模糊区域。此外，还提出了一个系统的大脑功能网络来揭示这些通道之间的关系，并进一步提高情绪识别性能。将贡献较大的通道连接起来，这些连接由动态图卷积网络学习，而其他通道则相互独立以消除干扰。在四个脑电情感识别数据集上的实验表明，Concat-aided Grad-CAM 可以进行细粒度的解释。此外，研究表明，学习的大脑功能网络可以提高基线的性能。值得注意的是，实验结果在多个实验中实现了最先进的性能。