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时间序列分析用于快速事件相关皮肤电反应。

Time-series analysis for rapid event-related skin conductance responses.

机构信息

Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, United Kingdom.

出版信息

J Neurosci Methods. 2009 Nov 15;184(2):224-34. doi: 10.1016/j.jneumeth.2009.08.005. Epub 2009 Aug 15.

Abstract

Event-related skin conductance responses (SCRs) are traditionally analysed by comparing the amplitude of individual peaks against a pre-stimulus baseline. Many experimental manipulations in cognitive neuroscience dictate paradigms with short inter trial intervals, precluding accurate baseline estimation for SCR measurements. Here, we present a novel and general approach to SCR analysis, derived from methods used in neuroimaging that estimate responses using a linear convolution model. In effect, the method obviates peak-scoring and makes use of the full SCR. We demonstrate, across three experiments, that the method has face validity in analysing reactions to a loud white noise and emotional pictures, can be generalised to paradigms where the shape of the response function is unknown and can account for parametric trial-by-trial effects. We suggest our approach provides greater flexibility in analysing SCRs than existing methods.

摘要

事件相关皮肤电反应 (SCR) 传统上通过比较单个峰值的振幅与刺激前基线来分析。认知神经科学中的许多实验操作都规定了短的试验间间隔的范式,这排除了 SCR 测量的准确基线估计。在这里,我们提出了一种新的、通用的 SCR 分析方法,该方法源自神经影像学中使用线性卷积模型估计响应的方法。实际上,该方法避免了峰值评分,并利用了完整的 SCR。我们通过三个实验证明,该方法在分析对大声白噪声和情绪图片的反应时具有表面有效性,可以推广到响应函数形状未知的范式,并可以解释参数逐试效应。我们建议我们的方法提供了比现有方法更大的灵活性来分析 SCR。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c468/2772899/5a30e20d951c/gr1.jpg

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