Linear spatial integration for single-trial detection in encephalography
Linear spatial integration for single-trial detection in encephalography
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DOI：
10.1006/nimg.2002.1212
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发表时间：
2002-09-01
期刊：
影响因子：
5.7
通讯作者：
Sajda, P
中科院分区：
文献类型：
--
作者：
Parra, L;Alvino, C;Sajda, P
脑电图（EEG）和脑磁图（MEG）的传统分析通常依赖于对多个试验进行平均，以提取两个或多个实验条件之间的统计相关差异。在这篇文章中，我们演示了单次试验检测线性集成信息在多个空间分布的传感器在一个预定义的时间窗口。我们报告了在三个不同的脑电图数据集上，A（z）的平均单次试验辨别性能近似为0.80，分数正确率在0.70和0.80之间。我们将我们的方法限制为线性积分，因为它允许计算的空间分布的歧视组件活动。在本组实验中，所得到的成分活动分布被示出为对应于与任务一致的功能神经解剖学（例如，对侧感觉-运动皮层和前扣带回）。我们的工作演示了如何学习脑电活动的最佳空间加权的纯数据驱动的方法可以验证对功能神经解剖学。(C)2002 Elsevier Science（美国）。
Conventional analysis of electroencephalography (EEG) and magnetoencephalography (MEG) often relies on averaging over multiple trials to extract statistically relevant differences between two or more experimental conditions. In this article we demonstrate single-trial detection by linearly integrating information over multiple spatially distributed sensors within a predefined time window. We report an average, single-trial discrimination performance of A(z) approximate to 0.80 and fraction correct between 0.70 and 0.80, across three distinct encephalographic data sets. We restrict our approach to linear integration, as it allows the computation of a spatial distribution of the discriminating component activity. In the present set of experiments the resulting component activity distributions are shown to correspond to the functional neuroanatomy consistent with the task (e.g., contralateral sensory-motor cortex and anterior cingulate). Our work demonstrates how a purely data-driven method for learning an optimal spatial weighting of encephalographic activity can be validated against the functional neuroanatomy. (C) 2002 Elsevier Science (USA).