Every pipeline here was published by a researcher. Open one, run it on your data, fork it, share what you build on top. No accounts, no paywalls.
Reference workflow for event-related potentials. Re-referencing, baseline correction, ICA, epoching, averaging.
Automated sleep staging pipeline with custom epoch overrides and quality flagging for human review.
Forward model from individual MRI, noise covariance estimation, eLORETA inverse with bootstrap CIs.
Modified K-means clustering on global field power peaks. Canonical 4-state segmentation with backfit.
Covariance-on-manifold classifier for 4-class motor imagery. Subject-specific calibration in under a minute.
Decompose power spectra into aperiodic background and periodic peaks. Group-level summary stats included.
PLV connectivity in 8–13 Hz, surrogate-tested significance, network metrics (small-worldness, modularity).
Deep-learning ICLabel categorization of ICA components. Auto-reject muscle/eye, flag others for review.
End-to-end deep decoder for visual stimulus classification. Cross-subject training with domain adaptation.
Artifact subspace reconstruction with sliding-window bad-block rejection. For ambulatory recordings.
Classic oddball paradigm pipeline. Subject-level templates, group grand average with cluster-permutation stats.
Quantitative EEG report for resting-state recordings. Per-band topomaps, asymmetry indices, PDF export.
Publishing a workflow takes one command and one minute. We auto-generate a cite-as block so any paper using your pipeline credits you. Reproducibility, by default.
# Publish from inside Signal Studio, or from the command line: $ signal-studio publish my_pipeline.sigs # Signal Studio validates the workflow, generates docs, # and prints the public URL. validating nodes... ok generating preview... ok writing cite-as block... ok published: signalstudio.app/wf/yourname/my-pipeline