神经活动变化的探索性数据分析
Explorative Data Analysis for Changes in Neural Activity
摘要：
神经记录是非平稳的时间序列，即它们的属性通常随时间变化。识别特定的变化，例如由学习任务引起的变化，可以揭示潜在的神经过程。然而，这种感兴趣的变化通常被强烈的不相关的变化所掩盖，这些变化可能是生理来源的或由于测量伪影。我们提出了一种新的算法来解开这种不同的非平稳性的原因，并以这种方式使更好的神经生理学解释更广泛的实验范式。一个关键因素是使用不同的时间尺度重复应用静止子空间分析（SSA）。我们的探索性方法的有用性证明了在模拟，理论和EEG实验与80脑机接口（BCI）的主题。
英文摘要：
Neural recordings are nonstationary time series, i.e. their properties typically change over time. Identifying specific changes, e.g. those induced by a learning task, can shed light on the underlying neural processes. However, such changes of interest are often masked by strong unrelated changes, which can be of physiological origin or due to measurement artifacts. We propose a novel algorithm for disentangling such different causes of non-stationarity and in this manner enable better neurophysiological interpretation for a wider set of experimental paradigms. A key ingredient is the repeated application of Stationary Subspace Analysis (SSA) using different temporal scales. The usefulness of our explorative approach is demonstrated in simulations, theory and EEG experiments with 80 Brain-Computer-Interfacing (BCI) subjects.
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