Neural component analysis: A spatial filter for electroencephalogram analysis

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.