Three-way Analysis of Multichannel EEG Data Using the PARAFAC and Tucker Models

Z. Rost'áková, R. Rosipal
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引用次数: 1

Abstract

Changes of hidden sources of the neural electrical activity of a brain over time, as represented by a continuously recorded multichannel electroencephalogram (EEG) at the scalp, can be detected by tensor or multiway decomposition of the EEG records. In this study, the performance of i) the constrained Tucker model and ii) the parallel factor analysis (PARAFAC) models are compared on real EEG data.
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基于PARAFAC和Tucker模型的多通道脑电数据三向分析
通过对EEG记录的张量或多路分解,可以检测到脑电活动隐藏源随时间的变化,如头皮连续记录的多通道脑电图(EEG)所示。在本研究中,比较了约束Tucker模型和平行因子分析(PARAFAC)模型在真实脑电数据上的性能。
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