Delis Ioannis, Onken Arno, Schyns Philippe G, Panzeri Stefano, Philiastides Marios G
Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, G12 8QB, United Kingdom; Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems@UniTn, Istituto Italiano di Tecnologia, Via Bettini 31, 38068, Rovereto (TN), Italy.
Neuroimage. 2016 Jun;133:504-515. doi: 10.1016/j.neuroimage.2016.03.043. Epub 2016 Mar 24.
We develop a novel methodology for the single-trial analysis of multichannel time-varying neuroimaging signals. We introduce the space-by-time M/EEG decomposition, based on Non-negative Matrix Factorization (NMF), which describes single-trial M/EEG signals using a set of non-negative spatial and temporal components that are linearly combined with signed scalar activation coefficients. We illustrate the effectiveness of the proposed approach on an EEG dataset recorded during the performance of a visual categorization task. Our method extracts three temporal and two spatial functional components achieving a compact yet full representation of the underlying structure, which validates and summarizes succinctly results from previous studies. Furthermore, we introduce a decoding analysis that allows determining the distinct functional role of each component and relating them to experimental conditions and task parameters. In particular, we demonstrate that the presented stimulus and the task difficulty of each trial can be reliably decoded using specific combinations of components from the identified space-by-time representation. When comparing with a sliding-window linear discriminant algorithm, we show that our approach yields more robust decoding performance across participants. Overall, our findings suggest that the proposed space-by-time decomposition is a meaningful low-dimensional representation that carries the relevant information of single-trial M/EEG signals.
我们开发了一种用于多通道时变神经影像信号单试次分析的新方法。我们引入了基于非负矩阵分解（NMF）的时空M/EEG分解，该方法使用一组非负的空间和时间成分来描述单试次M/EEG信号，这些成分通过带符号的标量激活系数进行线性组合。我们在一项视觉分类任务执行期间记录的脑电图数据集上说明了所提出方法的有效性。我们的方法提取了三个时间和两个空间功能成分，实现了对底层结构的紧凑而完整的表示，这验证并简洁地总结了先前研究的结果。此外，我们引入了一种解码分析，该分析允许确定每个成分的独特功能作用，并将它们与实验条件和任务参数相关联。特别是，我们证明了使用从识别出的时空表示中提取的特定成分组合，可以可靠地解码每个试次所呈现的刺激和任务难度。与滑动窗口线性判别算法相比，我们表明我们的方法在不同参与者之间产生了更稳健的解码性能。总体而言，我们的研究结果表明，所提出的时空分解是一种有意义的低维表示，它承载了单试次M/EEG信号的相关信息。
