Feature-space selection with banded ridge regression (Dupré la Tour et al., Neuroimage, 2022)
December 1, 2022
Encoding models identify the information represented in brain recordings, but fitting multiple models simultaneously presents several challenges. This paper describes how banded ridge regression can be used to solve these problems. Furthermore, several methods are proposed to address the computational challenge of fitting banded ridge regressions on large numbers of voxels and feature spaces. All implementations are released in an open-source Python package called Himalaya.