Background

Spatial filtering and source separation are valuable tools in the analysis of EEG data. However, despite the well known spatial localisation of individual cognitive processes within the brain, the available methods for source separation, such as the widely used blind source separation technique, do not take into account the spatial distributions and locations of sources. This can result in sub-optimal source identification.

New method

We present a new method for deriving a spatial filter for EEG data that attempts to identify sources that are maximally spatially distinct from one another in terms of the spatial distributions of their projections.

Results

We first evaluate our method with simulated EEG and show that it is able to separate EEG signals into components with distinct spatial distributions that closely resemble the original simulated sources. We also evaluate our method with real EEG and show it is able to identify a spatial filter that can be used to significantly improve classification accuracy of the P300 event-related potential (ERP).

Comparison with existing methods

We compare our method to a state of the art blind source separation methods, fast independent component analysis (ICA) and common spatial patterns (CSP). We evaluate the methods suitability for a common source separation application, analysis of ERPs.

Conclusions

Our results show that our method is well suited to identifying spatial filters for EEG analysis. This has potential applications in a wide range of EEG signal processing applications.

中文翻译：

---

神经成分分析：用于脑电图分析的空间滤波器

背景

在EEG数据分析中，空间过滤和源分离是有价值的工具。但是，尽管大脑中各个认知过程的空间定位众所周知，但是用于源分离的可用方法（例如广泛使用的盲源分离技术）并未考虑源的空间分布和位置。这可能导致源识别不理想。

新方法

我们提出了一种导出脑电数据的空间滤波器的新方法，该方法试图确定在其投影的空间分布方面彼此最大不同的源。

结果

我们首先用模拟的脑电图评估我们的方法，并表明它能够将脑电信号分离为具有与原始模拟源非常相似的不同空间分布的分量。我们还用真实的EEG对我们的方法进行了评估，并表明它能够识别空间过滤器，该过滤器可用于显着提高P300事件相关电位（ERP）的分类准确性。

与现有方法的比较

我们将我们的方法与最先进的盲源分离方法，快速独立成分分析（ICA）和常见空间模式（CSP）进行比较。我们评估方法对通用源分离应用程序的适用性，即对ERP的分析。

结论

我们的结果表明，我们的方法非常适合识别用于脑电图分析的空间滤波器。这在广泛的EEG信号处理应用中具有潜在的应用。