Writing & technical notes
Notes from the seam between the lab and the model.
Short write-ups on methods, tooling, and the gotchas that come from working with real, noisy, motion-corrupted data — plus longer essays on neuroscience and machine learning. [ placeholder entries — replace with real posts ]
I.Technical Notes03 entries
N01Decoding millisecond EEG through motion artifacts ↗A practical pipeline for recovering fast neural dynamics from movement-corrupted recordings — windowing, artifact rejection, and what actually survives.N02From 3 weeks to 2 days: parallelizing preprocessing ↗Restructuring a multivariate neuroimaging pipeline for HPC clusters without losing reproducibility.N03Graph features from functional connectomes ↗Extracting network statistics that actually predict clinical outcomes in TBI recovery.II.Essayson the blog
E01What neuroscience taught me about machine learning ↗[ Essay placeholder — pull the real post from the bearblog. ]