Multimodal and Multivariate Machine Learning Methods for Nonlinearly Coupled Oscillatory Systems

学习合适的表示，或从数据中提取有用的特征，是机器学习的基本问题之一。近年来，多模式神经成像已成为基础研究和临床诊断的重要工具。通过利用机器学习的方法，已经有可能加深我们对多模式神经数据的理解，并从大量的高维数据中提取新的见解，例如从EEG/EMG记录和血流动力学的同步测量(例如，fMRI的近红外光谱)获得的数据。然而，如果特定通道动力学之间的耦合是非线性的，那么目前使用的分析方法不能最优地提取潜在的公共因素(潜在来源)。这是因为这些方法要么不是真正的多模式，要么没有充分考虑到已建立的生成性模型和模式之间耦合类型的非线性性质。此外，今天的方法在准确性(例如，预测或回归质量方面的错误)和可解释性(即，关于隐藏的原因/来源的解释结果的能力)之间存在折衷。在第一部分(分析)中，我们将开发新的多变量方法，用于从多模式成像数据中同时提取非线性相互作用源。特别是，我们将专注于特定于领域的生成模型，以便找到最大限度地解释底层系统的耦合动力学的多通道数据的低维表示。同时，提取的来源将遵守领域特定的生成模型假设，因此在其中将是可解释的。具体地说，我们将开发新的多模式和多变量空间滤波方法，以揭示多模式神经成像数据中的共同来源，这些数据的动力学是非线性和非静态耦合的。通过寻求公共源空间表示，我们预计我们的方法不仅将在测量模式之间建立联系方面提供优异的性能，而且它们将克服前面提到的准确性和可解释性之间的权衡。在本项目的第二部分，我们将把新开发的方法应用于(计算)神经科学和神经技术领域的现有开放问题。我们希望能够对以下问题做出重大贡献：(I)事件相关电位(ERP)产生的潜在机制，(Ii)脑机接口(BCI)的新的无监督训练方法，以及(Iii)更好地理解脑电频谱功率和近红外光谱测量中的常见动力学，这反过来将导致BCI应用程序的卓越性能。

Learning appropriate representations, or extracting useful features from data, is one of the fundamentalproblems of Machine Learning. Recently, multimodal neuroimaging has become an important tool forbasic research and clinical diagnosis. By utilizing methods from machine learning, it has been possibleto further our understanding of multimodal neural data and extract novel insights from the multitudeof high dimensional data, such as obtained from EEG/EMG recordings and simultaneous measuresof hemodynamics (e.g. NIRS of fMRI). However, analysis methods that are currently being used arenot able to optimally extract the underlying common factors (latent sources) if the coupling betweenthe modality specific dynamics is nonlinear. This is due to the fact that either the methods are nottruly multimodal or they do not fully take into account established generative models and the nonlinearnature of the types of coupling between modalities. Furthermore, todays methods suffer from a tradeoffbetween accuracy (e.g. errors in terms of prediction or quality of regression) and interpretability (i.e. theability to interpret the resulting representations with respect to the hidden causes/sources).The proposed project is organized in two parts. In the first (analytical) part we will develop novelmultivariate methods for the simultaneous extraction of nonlinearly interacting sources from multimodalimaging data. In particular we will focus on domain specific generative models in order to find low dimensional representations of the multimodal data that maximally explain the coupled dynamics of theunderlying system. At the same time, the extracted sources will adhere to the domain specific generativemodel assumptions and will therefore be interpretable therein. Specifically we will develop novelmultimodal and multivariate spatial filtering methods that uncover common sources in multimodal neuroimaging data whose dynamics are nonlinearly and nonstantaneously coupled. By seeking common source space representations, we anticipate that our methods will not only provide excellent performance in terms of establishing a connection between measurement modalities, but that they will overcome the aforementioned tradeoff between accuracy and interpretability.In the second part of this project we will apply the newly developed methods to existing open questionsfrom the fields of (computational) neuroscience and neurotechnology. We expect to be able tocontribute substantially to questions concerning (i) the mechanisms underlying the generation of eventrelated potentials (ERP), (ii) novel unsupervised training methods for Brain-Computer Interfaces (BCI), and (iii) better understanding of common dynamics in EEG spectral power and NIRS measurements which in turn will lead to superior performance of BCI applications.