End-to-end decoding pipeline on real data

Three connected examples that walk through a full encoding-model decoding pipeline on a small extract of single-trial fMRI data (numerosity task, right numerosity-tuned parietal cortex):

1. Select responsive voxels from whole-brain R² by fitting a 2-component mixture and picking a threshold (FDR or posterior).

2. Decode trial-wise stimulus posteriors using a noise model whose covariance is regularised by geodesic distance on the cortical surface.

3. Quantify expected decoding uncertainty by simulating from the fitted model and re-decoding.

Each example is self-contained but share the same demo dataset, which is downloaded on first run via braincoder.utils.data.load_dehollander2024_npc.

Voxel selection via a 2-component R² mixture model

Voxel selection via a 2-component R² mixture model

Expected decoding uncertainty via simulate + decode

Expected decoding uncertainty via simulate + decode

Geodesic-regularised noise model for trial-wise decoding

Geodesic-regularised noise model for trial-wise decoding