Sharing and Integration of Cognitive Neuroscience Data: Metric and Pattern Matching across Heterogeneous ERP Datasets.

Sharing and Integration of Cognitive Neuroscience Data: Metric and Pattern Matching across Heterogeneous ERP Datasets.
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认知神经科学数据的共享和集成:跨异构 ERP 数据集的指标和模式匹配。

DOI:
10.1016/j.neucom.2012.01.028
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发表时间:
2012
期刊:
影响因子:
6
通讯作者:
Dou,Dejing
Dou,Dejing
中科院分区:
计算机科学2区
文献类型:
--
作者:
Liu,Haishan;Frishkoff,Gwen;Frank,Robert;Dou,Dejing

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在本文中,我们使用数据挖掘方法来解决两个挑战,在共享和整合的数据,从电生理(ERP)研究人类大脑功能。第一个挑战,ERP指标匹配,是识别来自不同研究实验室的ERP数据集中不同摘要特征(“指标”)之间的对应关系。第二个挑战,ERP模式匹配,是对齐这些数据集中的ERP模式或“组件”。我们在一个统一的框架内应对这两个挑战。这个框架的效用说明了一系列的实验,使用ERP数据集,旨在模拟异质性从三个来源:(a)不同的群体的受试者具有不同的模拟模式的大脑活动,(B)不同的测量方法,即,替代的空间和时间的指标,和(c)不同的模式,反映了替代模式分析技术的使用。与真实的ERP数据不同,模拟数据来自已知的源模式,为评估所提出的匹配方法提供了金标准。使用这种方法,我们证明了所提出的方法优于已知的现有方法,因为它利用基于聚类的结构,从而实现了ERP数据的多维(空间和时间)属性的细粒度表示。
In the present paper, we use data mining methods to address two challenges in the sharing and integration of data from electrophysiological (ERP) studies of human brain function. The first challenge, ERP metric matching, is to identify correspondences among distinct summary features (“metrics”) in ERP datasets from different research labs. The second challenge, ERP pattern matching, is to align the ERP patterns or “components” in these datasets. We address both challenges within a unified framework. The utility of this framework is illustrated in a series of experiments using ERP datasets that are designed to simulate heterogeneities from three sources: (a) different groups of subjects with distinct simulated patterns of brain activity, (b) different measurement methods, i.e, alternative spatial and temporal metrics, and (c) different patterns, reflecting the use of alternative pattern analysis techniques. Unlike real ERP data, the simulated data are derived from known source patterns, providing a gold standard for evaluation of the proposed matching methods. Using this approach, we demonstrate that the proposed method outperforms well-known existing methods, because it utilizes cluster-based structure and thus achieves finer-grained representation of the multidimensional (spatial and temporal) attributes of ERP data.
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DOI: --
发表时间: 2022
期刊:
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Barrett;B;Ohta;H.;and Trencher;G. "Prospects for Acceleration of Socio-Technical Transitions for Deep Decarbonization," pp. 400-414;青山瑠妙;福田円;舒旻「中国:EUとの脆弱な相互依存」pp. 247-271;福田円;青山瑠妙;青山瑠妙;青山瑠妙;福田円;青山瑠妙;福田円
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