Interpreting single trial data using groupwise regularisation
Interpreting single trial data using groupwise regularisation
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DOI：
10.1016/j.neuroimage.2009.02.041
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发表时间：
2009-07-01
期刊：
影响因子：
5.7
通讯作者：
Heskes, Tom
中科院分区：
文献类型：
--
作者：
van Gerven, Marcel;Hesse, Christian;Heskes, Tom
单变量统计方法通常用于神经成像数据的分析，但无法检测大脑活动的不同组成部分之间的微妙相互作用。相比之下，使用分类作为基础的多变量方法非常适合检测这种相互作用，允许在单个试验水平上分析神经影像学数据。然而，多变量方法通常为每个分量分配非零贡献，使得结果的解释变得麻烦。本文介绍了groupwise正则化作为一种新的方法，寻找稀疏，因此易于解释，模型，能够预测的实验条件下，单次试验属于。此外，通过将从数据中提取的被认为属于一起的特征放置到组中，可以以各种方式约束所获得的模型。为了从数据中学习模型，我们引入了一种新的算法，该算法利用了本文导出的稳定性条件。该算法是用来分类多传感器记录的运动想象任务的EEG信号使用（groupwise）正则化逻辑回归作为基础分类。我们表明，正则化大大减少了功能的数量，而不会降低分类率。这提高了模型的可解释性，因为它发现了数据中的特征，例如想象运动对侧的运动皮层中的mu和beta去极化。通过选择特定的分组，我们可以约束正则化的解决方案，以便使用更少数量的传感器或获得一个模型，该模型可以很好地概括受试者。识别少量最能解释数据的特征组使分组正则化成为单次试验分析的有用新工具。(C)2009 Elsevier Inc. All rights reserved.
Univariate statistical approaches are often used for the analysis of neuroimaging data but are unable to detect subtle interactions between different components of brain activity. In contrast, multivariate approaches that use classification as a basis are well-suited to detect such interactions, allowing the analysis of neuroimaging data on the single trial level. However, multivariate approaches typically assign a non-zero contribution to every component, making interpretation of the results troublesome. This paper introduces groupwise regularisation as a novel method for finding sparse, and therefore easy to interpret, models that are able to predict the experimental condition to which single trials belong. Furthermore, the obtained models can be constrained in various ways by placing features extracted from the data that are thought to belong together into groups. In order to learn models from data, we introduce a new algorithm that makes use of stability conditions that have been derived in this paper. The algorithm is used to classify multisensor EEG signals recorded for a motor imagery task using (groupwise) regularised logistic regression as the underlying classifier. We show that regularisation dramatically reduces the number of features without reducing the classification rate. This improves model interpretability as it finds features in the data such as mu and beta desynchronisation in the motor cortex contralateral to the imagined movement. By choosing particular groupings we can constrain the regularised Solutions such that a lower number of sensors is used or a model is obtained that generalises well over subjects. The identification of a small number of groups of features that best explain the data make groupwise regularisation a useful new tool for single trial analysis. (C) 2009 Elsevier Inc. All rights reserved.