Measurement of small magnetic fields requires shielding. For example, magnetoencephalography (MEG) measures small fields arising within the brain, in the presence of environmental fields several orders of magnitude larger. A common solution is passive shielding, but its cost, bulk and limited effectiveness motivate the alternative approach of active shielding, which comes in two flavours: feed-back and feed-forward. In feed-back shielding (the most common design), coils are driven from sensors that measure the corrected field. In feed-forward shielding (less common), coils are driven from sensors that measure the uncorrected field. The latter has the potential of providing better performance, but its implementation is hobbled by an inevitable coupling between coils and reference sensors. This paper describes an approach that employs a data-driven ``decoupling matrix" to suppress the coupling, enabling greater flexibility to achieve effective shielding across a wide frequency range. The method is evaluated by simulation, first using a simplified "flat world" in which the principle is clearly visualised, and then based on the more realistic geometry of a standard MEG system. Performance appears to be limited only by spatial sampling and numerical resolution. The method, which is data-driven and does not require geometric calculations, tight-tolerance fabrication, or manual calibration, is complementary with standard feed-back shielding methods. Active shielding is of particular interest in the context of a new generation of optically-pumped magnetometers (OPM), as the lower cost and reduced constraints of these new systems relative to SQUID-based systems provide an incentive to likewise reduce the cost and constraints of shielding.

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