In this work, nonlinear temporal features from multi-channel EEGs are used for the classification of Alzheimer's disease patients from healthy individuals. This was achieved by temporal manifold learning using Gaussian Process Latent Variable Models (GPLVM) as a nonlinear dimensionality reduction technique. Classification of the extracted features was undertaken using a nonlinear Support Vector Machine. Comparisons were made against the linear counterpart, Principle Component Analysis while exploring the effect of the time window or EEG epoch length used. It was demonstrated that temporal manifold learning using GPLVM is better in extracting features that attain high separability and prediction accuracy. This work aims to set the significance of using GPLVM temporal manifold learning for EEG feature extraction in the classification of Alzheimer's disease.

中文翻译：

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高斯过程潜变量模型对阿尔茨海默氏病患者脑电图记录的非线性分类

在这项工作中，来自多通道脑电图的非线性时间特征被用于分类来自健康个体的阿尔茨海默氏病患者。这是通过使用高斯过程潜变量模型（GPLVM）作为非线性降维技术的时间流形学习来实现的。使用非线性支持向量机对提取的特征进行分类。在探讨时间窗或脑电图历元长度的影响时，与线性对应项，主成分分析进行了比较。结果表明，使用GPLVM进行时间流形学习在提取可实现高可分离性和预测精度的特征方面效果更好。