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.

翻译：脑电图（EEG）是一种记录大脑活动的方法，在疾病分类、情绪检测和脑机接口等应用中展现出巨大潜力。深度学习的近期进展提升了脑电图分类性能，但模型的可解释性仍是问题。为应对这一可解释性的关键局限，我们提出了KnowEEG——一种新颖的可解释机器学习方法，用于脑电图分类。KnowEEG提取了一套全面的单电极特征，通过统计检验进行筛选，并整合了电极间连接性统计量。这些特征随后输入我们改进的随机森林模型（融合森林），该模型在构建森林决策树时平衡了单电极统计量与电极间连接特征。通过融合通用时间序列领域和脑电图特定领域的知识，KnowEEG在五项不同分类任务中达到或超越了当前最先进的深度学习模型性能：情绪检测、脑力负荷分类、睁眼/闭眼检测、异常脑电图分类和事件检测。除高性能外，KnowEEG通过可理解特征的重要性评分提供了内在可解释性。我们以睁眼/闭眼分类任务为例，证明这种可解释性可用于发掘关于类别的知识。当前神经科学文献证实了睁眼/闭眼分类任务中所发现知识的正确性。因此，KnowEEG将在医疗健康等脑电图可解释性至关重要的领域产生重要影响。