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 non-stationary, 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 requires 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, pain), we show that this method outperforms widely-used stationary filters and allows identifying novel ERP components masked by large widespread activity. Implementation of the LSA filter in Matlab is freely available to download.

中文翻译：

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局部空间分析 (LSA)：易于使用的自适应空间脑电图滤波器

空间 EEG 滤波器广泛用于隔离事件相关电位 (ERP) 组件。最常用的空间过滤器（例如平均参考和表面拉普拉斯算子）是静止的。固定滤波器在概念上简单、易于使用且计算速度快，但都假设 EEG 信号不会随传感器和时间发生变化。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（例如独立成分分析，ICA；和主成分分析，PCA）直接从数据的空间属性推断它们的权重。因此，它们不受固定滤波器缺点的影响。自适应滤波器的问题在于，理解它们的工作原理以及如何解释它们的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、易于使用且概念上简单的自适应滤波器（局部空间分析，LSA），用于突出显示被大规模广泛活动掩盖的局部组件。这种方法利用存储在刺激诱发的神经活动的逐次试验变异性中的统计信息来自适应地估计每个时间点的空间滤波器参数。使用模拟数据和由四种不同感官模式（听觉、视觉、触觉、疼痛）刺激引起的真实 ERP，我们表明该方法优于广泛使用的固定过滤器，并允许识别被广泛广泛活动掩盖的新型 ERP 组件。