Cluster-based permutation tests are widely used for analyzing MEG data, even though they are limited to cluster-level inference and do not provide spatially resolved effect estimates. We propose a regression-based framework for modeling brain activity directly on the cortical surface. Spatial effects are represented using Wendland radial basis functions, and model-based gradient boosting is employed for data-driven selection and estimation of localized activation patterns. This yields interpretable, spatially resolved effect estimates while mitigating the need for extensive multiple testing correction. In a simulation study with heterogeneous signal structures, the proposed approach recovers localized effects that are difficult to detect using cluster-based methods. An application to experimental MEG data further illustrates its ability to reveal spatially specific activation patterns. Overall, this unified framework provides a flexible and interpretable approach for modeling cortical surface data within a unified statistical setting.

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