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@@ -65,7 +65,7 @@ When one does this in a "searchlight" pattern across the brain, the result is a
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## Functionality
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The core functionality of MNE-RSA consists of an efficient pipeline that operates on NumPy arrays, starting from "searchlight" (i.e. multi-dimensional sliding window) indexing, to cross-validated computation of RDMs, to the comparison with "model" RDMs to produce RSA values.
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On top of the general purpose pipeline, MNE-RSA exposes functions that operate on MNE-Python (EEG, MEG) and Nibabel (fMRI) objects and also return the resulting RSA values as such objects.
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On top of the general purpose pipeline, MNE-RSA exposes functions that operate on MNE-Python (EEG, MEG) [@Gramfort2013]and Nibabel (fMRI)[@Brett2025] objects and also return the resulting RSA values as such objects.
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Those functions leverage the available metadata, such as the sensor layout, edges of cortical 3D meshes, and voxel sizes, to present a more intuitive API.
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MNE-RSA supports all the distance metrics in `scipy.spatial.distance` for computing RDMs and the following metrics for comparing RDMs:
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