论文标题
重新思考显着性图：一种解释基于脑电图的深度学习模型的上下文感知的扰动方法
Rethinking Saliency Map: An Context-aware Perturbation Method to Explain EEG-based Deep Learning Model
论文作者
论文摘要
深度学习被广泛用于解码脑电图（EEG）信号。但是，很少有尝试专门研究如何解释基于脑电图的深度学习模型的尝试。我们进行了审查，以总结说明基于脑电图的深度学习模型的现有作品。不幸的是，我们发现没有适当的方法来解释它们。基于脑电图数据的特征，我们建议一种从原始脑电图的角度来产生显着图的上下文感知扰动方法。此外，我们还可以证明，可以使用上下文信息来抑制基于EEG的深度学习模型中的工件。实际上，一些用户可能想要一个简单的解释版本，这仅表示一些功能作为显着点。为此，我们提出了一种可选的区域限制策略来限制突出显示的区域。为了验证我们的想法并与其他方法进行比较，我们选择了三个基于EEG的代表性模型来在情绪EEG数据集DEAP上实施实验。实验的结果支持我们方法的优势。
Deep learning is widely used to decode the electroencephalogram (EEG) signal. However, there are few attempts to specifically investigate how to explain the EEG-based deep learning models. We conduct a review to summarize the existing works explaining the EEG-based deep learning model. Unfortunately, we find that there is no appropriate method to explain them. Based on the characteristic of EEG data, we suggest a context-aware perturbation method to generate a saliency map from the perspective of the raw EEG signal. Moreover, we also justify that the context information can be used to suppress the artifacts in the EEG-based deep learning model. In practice, some users might want a simple version of the explanation, which only indicates a few features as salient points. To this end, we propose an optional area limitation strategy to restrict the highlighted region. To validate our idea and make a comparison with the other methods, we select three representative EEG-based models to implement experiments on the emotional EEG dataset DEAP. The results of the experiments support the advantages of our method.
