Multiview Classification and Dimensionality Reduction of Scalp and Intracranial EEG Data through Tensor Factorisation
Multiview Classification and Dimensionality Reduction of Scalp and Intracranial EEG Data through Tensor Factorisation
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DOI：
10.1007/s11265-016-1164-z
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发表时间：
2018-02-01
影响因子：
1.8
通讯作者：
Sanei, Saeid
中科院分区：
文献类型：
--
作者：
Spyrou, Loukianos;Kouchaki, Samaneh;Sanei, Saeid
脑电图（EEG）信号是发生在大脑特定空间、频率和时间位置的各种神经过程的混合物。在分类范式中，开发了能够区分这些过程的算法。在这项工作中，我们将张量分解应用于一组癫痫患者的EEG数据，并将数据分解为三种模式；空间、时间和频率，每个模式都包含一些分量或特征。我们在不同的特征集上训练单独的分类器，这些特征集对应于这些模式和组件的互补组合，并测试每个特征集的分类精度。然后可以分析对各自空间、时间或频率特征的分类准确性的相对影响，并作出有用的解释。此外，我们表明，通过张量分解，我们可以通过评估每个模式中组件的数量来执行降维，也可以通过拒绝对分类精度贡献不显著的组件来执行降维。
Electroencephalography (EEG) signals arise as mixtures of various neural processes which occur in particular spatial, frequency, and temporal brain locations. In classification paradigms, algorithms are developed that can distinguish between these processes. In this work, we apply tensor factorisation to a set of EEG data from a group of epileptic patients and factorise the data into three modes; space, time, and frequency with each mode containing a number of components or signatures. We train separate classifiers on various feature sets corresponding to complementary combinations of those modes and components and test the classification accuracy for each set. The relative influence on the classification accuracy of the respective spatial, temporal, or frequency signatures can then be analysed and useful interpretations can be made. Additionaly, we show that through tensor factorisation we can perform dimensionality reduction by evaluating the classification performance with regards to the number of components in each mode and also by rejecting components with insignificant contribution to the classification accuracy.