Identifying abnormal patterns in electroencephalography (EEG) remains the cornerstone of diagnosing several neurological diseases. The current clinical EEG review process relies heavily on expert visual review, which is unscalable and error-prone. In an effort to augment the expert review process, there is a significant interest in mining population-level EEG patterns using unsupervised approaches. Current approaches rely either on two-dimensional decompositions (e.g., principal and independent component analyses) or deep representation learning (e.g., auto-encoders, self-supervision). However, most approaches do not leverage the natural multi-dimensional structure of EEGs and lack interpretability. In this study, we propose a tensor decomposition approach using the canonical polyadic decomposition to discover a parsimonious set of population-level EEG patterns, retaining the natural multi-dimensional structure of EEGs (time x space x frequency). We then validate their clinical value using a cohort of patients including varying stages of cognitive impairment. Our results show that the discovered patterns reflect physiologically meaningful features and accurately classify the stages of cognitive impairment (healthy vs mild cognitive impairment vs Alzheimer's dementia) with substantially fewer features compared to classical and deep learning-based baselines. We conclude that the decomposition of population-level EEG tensors recovers expert-interpretable EEG patterns that can aid in the study of smaller specialized clinical cohorts.

翻译：脑电图（EEG）中异常模式的识别仍然是诊断多种神经系统疾病的基石。当前的临床脑电图审查过程高度依赖专家目视检查，这种方法缺乏可扩展性且容易出错。为了增强专家审查过程，利用无监督方法挖掘群体级脑电图模式引起了广泛关注。现有方法要么依赖二维分解（例如主成分分析和独立成分分析），要么依赖深度表示学习（例如自编码器、自监督学习）。然而，大多数方法未能利用脑电图固有的多维结构，且缺乏可解释性。在本研究中，我们提出了一种基于张量分解的方法，使用典型多分量分解来发现一组简约的群体级脑电图模式，同时保留脑电图固有的多维结构（时间×空间×频率）。随后，我们利用包含不同认知障碍阶段的患者队列验证了其临床价值。结果表明，所发现的模式反映了具有生理意义的特征，并且在使用显著少于经典和深度学习基线方法特征的情况下，能够准确分类认知障碍阶段（健康 vs 轻度认知障碍 vs 阿尔茨海默病痴呆）。我们得出的结论是，群体级脑电图张量的分解能够恢复专家可解释的脑电图模式，有助于研究规模较小的专业临床队列。