Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia.
School of Psychology, Social Work and Social Policy, University of South Australia, Adelaide, Australia.
Neuroinformatics. 2019 Jan;17(1):27-42. doi: 10.1007/s12021-018-9375-z.
In recent years, neuroimaging research in cognitive neuroscience has increasingly used multivariate pattern analysis (MVPA) to investigate higher cognitive functions. Here we present DDTBOX, an open-source MVPA toolbox for electroencephalography (EEG) data. DDTBOX runs under MATLAB and is well integrated with the EEGLAB/ERPLAB and Fieldtrip toolboxes (Delorme and Makeig 2004; Lopez-Calderon and Luck 2014; Oostenveld et al. 2011). It trains support vector machines (SVMs) on patterns of event-related potential (ERP) amplitude data, following or preceding an event of interest, for classification or regression of experimental variables. These amplitude patterns can be extracted across space/electrodes (spatial decoding), time (temporal decoding), or both (spatiotemporal decoding). DDTBOX can also extract SVM feature weights, generate empirical chance distributions based on shuffled-labels decoding for group-level statistical testing, provide estimates of the prevalence of decodable information in the population, and perform a variety of corrections for multiple comparisons. It also includes plotting functions for single subject and group results. DDTBOX complements conventional analyses of ERP components, as subtle multivariate patterns can be detected that would be overlooked in standard analyses. It further allows for a more explorative search for information when no ERP component is known to be specifically linked to a cognitive process of interest. In summary, DDTBOX is an easy-to-use and open-source toolbox that allows for characterising the time-course of information related to various perceptual and cognitive processes. It can be applied to data from a large number of experimental paradigms and could therefore be a valuable tool for the neuroimaging community.
近年来，认知神经科学中的神经影像学研究越来越多地使用多元模式分析（MVPA）来研究更高阶的认知功能。这里我们介绍 DDTBOX，这是一个用于脑电图（EEG）数据的开源 MVPA 工具箱。DDTBOX 在 MATLAB 下运行，与 EEGLAB/ERPLAB 和 Fieldtrip 工具箱高度集成（Delorme 和 Makeig 2004；Lopez-Calderon 和 Luck 2014；Oostenveld 等人，2011）。它在感兴趣事件前后的事件相关电位（ERP）振幅数据模式上训练支持向量机（SVM），用于实验变量的分类或回归。这些振幅模式可以在空间/电极（空间解码）、时间（时间解码）或两者（时空解码）上提取。DDTBOX 还可以提取 SVM 特征权重，基于随机标签解码生成群体水平统计检验的经验机会分布，提供人群中可解码信息的流行率估计，并进行多种多重比较校正。它还包括用于单个体和群体结果的绘图功能。DDTBOX 补充了传统的 ERP 成分分析，因为可以检测到细微的多元模式，而这些模式在标准分析中可能会被忽略。当没有已知的 ERP 成分与特定的感兴趣认知过程相关联时，它还可以进一步允许更具探索性的信息搜索。总之，DDTBOX 是一个易于使用的开源工具箱，可用于描述与各种感知和认知过程相关的信息的时程。它可以应用于来自大量实验范式的数据，因此对于神经影像学社区来说是一个有价值的工具。
