Abstract

A spatial filter combines signals from multiple sensors to create a virtual channel that emphasizes specific brain activity while reducing interference. Spatial filters range from simple, fixed configurations—such as rereferencing, gradient, or Laplacian filters—to more sophisticated, data-driven approaches like beamforming or independent component analysis (ICA). Although the underlying principle is simple, understanding a spatial filter's behavior can be challenging because of the high dimensionality of the data and the multiple “spaces” involved—those of sources, sensors, fields, and signals. This paper examines the properties and limitations of spatial filters, focusing on the idea of a virtual electrode—a synthetic signal formed by combining channels from noninvasive techniques such as EEG or MEG. While a spatial filter can fully suppress some sources, it cannot perfectly isolate a single source while rejecting all others, as a real electrode could. This places clear limits on what a virtual electrode can represent. I here suggest an alternative view of it as a virtual scalpel—a tool for refining recorded data rather than capturing activity of a single neural source. Just as temporal filters shape signals over time, spatial filters are key tools for improving the clarity and interpretability of brain recordings.