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首页> 外文期刊>Journal of Neurophysiology >Local spatial analysis: an easy-to-use adaptive spatial EEG filter.
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Local spatial analysis: an easy-to-use adaptive spatial EEG filter.

机译：局部空间分析：易于使用的自适应空间EEG滤波器。

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Spatial EEG filters are widely used to isolate event-related potential (ERP) components. The most commonly used spatial filters (e.g., the average reference and the surface Laplacian) are "stationary." Stationary filters are conceptually simple, easy to use, and fast to compute, but all assume that the EEG signal does not change across sensors and time. Given that ERPs are intrinsically nonstationary, applying stationary filters can lead to misinterpretations of the measured neural activity. In contrast, "adaptive" spatial filters (e.g., independent component analysis, ICA; and principal component analysis, PCA) infer their weights directly from the spatial properties of the data. They are, thus, not affected by the shortcomings of stationary filters. The issue with adaptive filters is that understanding how they work and how to interpret their output require advanced statistical and physiological knowledge. Here, we describe a novel, easy-to-use, and conceptually simple adaptive filter (local spatial analysis, LSA) for highlighting local components masked by large widespread activity. This approach exploits the statistical information stored in the trial-by-trial variability of stimulus-evoked neural activity to estimate the spatial filter parameters adaptively at each time point. Using both simulated data and real ERPs elicited by stimuli of four different sensory modalities (audition, vision, touch, and pain), we show that this method outperforms widely used stationary filters and allows to identify novel ERP components masked by large widespread activity. Implementation of the LSA filter in MATLAB is freely available to download. NEW & NOTEWORTHY EEG spatial filtering is important for exploring brain function. Two classes of filters are commonly used: stationary and adaptive. Stationary filters are simple to use but wrongly assume that stimulus-evoked EEG responses (ERPs) are stationary. Adaptive filters do not make this assumption but require solid statistical and physiological knowledge. Bridging this gap, we present local spatial analysis (LSA), an adaptive, yet computationally simple, spatial filter based on linear regression that separates local and widespread brain activity (https://www.iannettilab.net/lsa.html or https://github.com/rorybufacchi/LSA-filter).
机译：空间脑电滤波器广泛用于分离事件相关电位（ERP）成分。最常用的空间滤波器（例如，平均参考和表面拉普拉斯）是“静止的”平稳滤波器概念简单，易于使用，计算速度快，但都假设EEG信号不随传感器和时间变化。鉴于ERP本质上是非平稳的，应用平稳滤波器可能会导致对测量的神经活动的误解。相比之下，“自适应”空间滤波器（例如独立分量分析（ICA）和主分量分析（PCA））直接从数据的空间特性推断其权重。因此，它们不受固定过滤器缺点的影响。自适应滤波器的问题在于，了解它们如何工作以及如何解释其输出需要先进的统计和生理知识。在这里，我们描述了一种新颖、易于使用且概念简单的自适应滤波器（local spatial analysis，LSA），用于突出显示被大规模广泛活动掩盖的局部组件。该方法利用刺激诱发神经活动的逐试变异性中存储的统计信息，在每个时间点自适应地估计空间滤波器参数。通过使用模拟数据和四种不同感觉模式（听觉、视觉、触觉和疼痛）刺激诱发的真实ERP，我们表明，该方法优于广泛使用的固定过滤器，并允许识别被大规模广泛活动掩盖的新ERP成分。LSA滤波器在MATLAB中的实现可免费下载。新的和值得注意的脑电图空间滤波对于探索大脑功能非常重要。通常使用两类滤波器：平稳滤波器和自适应滤波器。平稳滤波器使用简单，但错误地认为刺激诱发脑电反应（ERPs）是平稳的。自适应滤波器不做这个假设，但需要坚实的统计和生理知识。为了弥补这一差距，我们提出了局部空间分析（LSA），这是一种基于线性回归的自适应但计算简单的空间滤波器，用于分离局部和广泛的大脑活动(https://www.iannettilab.net/lsa.html或https://github.com/rorybufacchi/LSA-filter).

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