标题： KnowEEG：基于可解释知识驱动的脑电分类 显示英文标题

标题： KnowEEG: Explainable Knowledge Driven EEG Classification

摘要： 脑电图（EEG）是一种记录大脑活动的方法，在从疾病分类到情感检测和脑-机接口的应用中显示出巨大的潜力。深度学习的最新进展提高了EEG分类性能，但模型可解释性仍然是一个问题。为了解决这一关键局限性，我们引入了KnowEEG；这是一种新颖的可解释机器学习方法，用于EEG分类。KnowEEG提取了一整套每电极特征，使用统计测试对其进行过滤，并整合了电极间连接统计信息。然后将这些特征输入我们的修改后的随机森林模型（融合森林），该模型在森林中生长树时平衡了每电极统计信息与电极间连接特征。通过结合来自广义时间序列和EEG特定领域的知识，KnowEEG在五个不同的分类任务中实现了与最先进的深度学习模型相当或更高的性能：情感检测、心理工作量分类、睁眼/闭眼检测、异常EEG分类和事件检测。除了高性能外，KnowEEG还通过特征重要性分数提供了内在的可解释性，以理解特征。我们通过闭眼/睁眼分类任务的例子证明，这种可解释性可以用来发现关于类别的知识。对于闭眼/睁眼分类，当前的神经科学文献证明了所发现的知识是正确的。因此，KnowEEG在EEG可解释性至关重要的领域，如医疗保健领域，将产生重大影响。 显示英文摘要

摘要： Electroencephalography (EEG) is a method of recording brain activity that shows significant promise in applications ranging from disease classification to emotion detection and brain-computer interfaces. Recent advances in deep learning have improved EEG classification performance yet model explainability remains an issue. To address this key limitation of explainability we introduce KnowEEG; a novel explainable machine learning approach for EEG classification. KnowEEG extracts a comprehensive set of per-electrode features, filters them using statistical tests, and integrates between-electrode connectivity statistics. These features are then input to our modified Random Forest model (Fusion Forest) that balances per electrode statistics with between electrode connectivity features in growing the trees of the forest. By incorporating knowledge from both the generalized time-series and EEG-specific domains, KnowEEG achieves performance comparable to or exceeding state-of-the-art deep learning models across five different classification tasks: emotion detection, mental workload classification, eyes open/closed detection, abnormal EEG classification, and event detection. In addition to high performance, KnowEEG provides inherent explainability through feature importance scores for understandable features. We demonstrate by example on the eyes closed/open classification task that this explainability can be used to discover knowledge about the classes. This discovered knowledge for eyes open/closed classification was proven to be correct by current neuroscience literature. Therefore, the impact of KnowEEG will be significant for domains where EEG explainability is critical such as healthcare.