Abstract Analysis of changes in the brain neural electrical activity measured by the electroencephalogram (EEG) plays a crucial role in the area of brain disorder diagnostics. The elementary latent sources of the brain neural activity can be extracted by a tensor decomposition of continuously recorded multichannel EEG. Parallel factor analysis (PARAFAC) is a powerful approach for this purpose. However, the assumption of the same number of factors in each dimension of the PARAFAC model may be restrictive when applied to EEG data. In this article we discuss the potential benefits of an alternative tensor decomposition method – the Tucker model. We analyze situations, where in comparison to the PARAFAC solution, the Tucker model provides a more parsimonious representation of the EEG data decomposition. We show that this more parsimonious representation of EEG is achieved without reducing the ability to explain variance. We analyze EEG records of two patients after ischemic stroke and we focus on the extraction of specific sensorimotor oscillatory sources associated with motor imagery during neurorehabilitation training. Both models provided consistent results. The advantage of the Tucker model was a compact structure with only two spatial signatures reflecting the expected lateralized activation of the detected subject-specific sensorimotor rhythms.

中文翻译：

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非负塔克分解与平行因子分析在人类脑电节律识别和测量中的比较

摘要 通过脑电图 (EEG) 测量的大脑神经电活动的变化分析在大脑疾病诊断领域起着至关重要的作用。大脑神经活动的基本潜在来源可以通过连续记录的多通道 EEG 的张量分解来提取。并行因子分析 (PARAFAC) 是实现此目的的强大方法。然而，当应用于 EEG 数据时，PARAFAC 模型的每个维度中因子数量相同的假设可能会受到限制。在本文中，我们讨论了另一种张量分解方法——Tucker 模型的潜在好处。我们分析了与 PARAFAC 解决方案相比，Tucker 模型提供了更简洁的 EEG 数据分解表示的情况。我们表明，这种更简洁的 EEG 表示是在不降低解释方差的能力的情况下实现的。我们分析了缺血性中风后两名患者的 EEG 记录，我们专注于在神经康复训练期间提取与运动意象相关的特定感觉运动振荡源。两种模型都提供了一致的结果。Tucker 模型的优点是结构紧凑，只有两个空间特征反映了检测到的特定于受试者的感觉运动节律的预期侧向激活。两种模型都提供了一致的结果。Tucker 模型的优点是结构紧凑，只有两个空间特征反映了检测到的特定于受试者的感觉运动节律的预期侧向激活。两种模型都提供了一致的结果。Tucker 模型的优点是结构紧凑，只有两个空间特征反映了检测到的特定于受试者的感觉运动节律的预期侧向激活。