An engine for cognitive and computational neuroscience — from feature extraction and dimensionality reduction to trajectory analysis, classical machine learning, and foundation-model decoding, with publication-grade visualization and automated reporting.
``coco-pipe`` provides reusable, leakage-safe components for M/EEG and related biosignal research. Each module works on its own or as part of an end-to-end workflow, and they all speak the same :class:`~coco_pipe.io.DataContainer`. .. grid:: 1 2 3 3 :gutter: 3 :class-container: sd-mt-4 .. grid-item-card:: 🚀 Getting Started :link: getting_started :link-type: doc Install ``coco-pipe`` and run your first end-to-end workflow. .. grid-item-card:: 📦 Data & IO :link: io/index :link-type: doc Loading (tabular, BIDS, embeddings), the ``DataContainer``, quality control, and persistence. .. grid-item-card:: 🌊 Descriptors :link: descriptors/index :link-type: doc Signal feature extraction — spectral, parametric, and complexity families. .. grid-item-card:: 🧠 Decoding — Classical ML :link: decoding/index :link-type: doc Leakage-safe classification & regression with scikit-learn estimators, cross-validation, tuning, and statistical assessment. .. grid-item-card:: 🤖 Decoding — Foundation Models :link: decoding/foundation_models :link-type: doc Pretrained backbones (frozen, fine-tuned, LoRA/QLoRA). In the decoding module, but a wholly separate design. .. grid-item-card:: 🌀 Dimensionality Reduction :link: dim_reduction/index :link-type: doc PCA, UMAP, PHATE, PaCMAP and 15+ reducers behind one interface, with quality metrics and method comparison. .. grid-item-card:: 📈 Trajectory Analysis :link: dim_reduction/trajectories :link-type: doc Kinematics and time-resolved group separation over native 3D embedding tensors. .. grid-item-card:: 📊 Visualization :link: viz/index :link-type: doc Mirrored Matplotlib and Plotly backends, one theme, exploratory to publication-ready figures. .. grid-item-card:: 📄 Reports :link: report/index :link-type: doc Self-contained, interactive HTML reports — lineage-aware and offline-ready. Where to go next ---------------- .. grid:: 1 1 3 3 :gutter: 2 .. grid-item-card:: User Guide :link: user_guide :link-type: doc Scientific guides for every module. .. grid-item-card:: API Reference :link: api_reference :link-type: doc Public API and the full module index. .. grid-item-card:: Examples :link: auto_examples/index :link-type: doc Executable, end-to-end gallery. Our vision ---------- ``coco-pipe`` is, first, an **engine** that brings the tools of cognitive and computational neuroscience under one roof — feature extraction, dimensionality reduction, trajectory analysis, classical decoding, and foundation models — all speaking one data structure. On top of that engine we are building **end-to-end pipelines**: throw your preprocessed data at them and, with a few CLI commands, run complementary analyses to understand your data — then move from broad exploration to focused, targeted analysis, again powered by the engine. We start with **M/EEG**, and aim to extend to other modalities as the engine matures. .. seealso:: Read the full :doc:`vision` for the roadmap and design principles. .. toctree:: :maxdepth: 1 :hidden: getting_started user_guide api_reference Examples