rosoku 0.0.7

A flexible callback-based toolkit for EEG/BCI experiments that lets you define data loading and preprocessing freely, while rosoku handles training, evaluation, and result logging for conventional and deep-learning models.

🕯️ Rosoku — A Flexible EEG/BCI Experiment Pipeline Toolkit

Rosoku is a research-oriented Python framework for running reproducible EEG/BCI experiments with both conventional machine-learning models and deep-learning models.

It bridges the gap between high-level EEG/BCI frameworks (such as MOABB and Braindecode) and low-level machine-learning libraries (such as scikit-learn and PyTorch), by providing structured yet flexible experiment pipelines without hiding critical details.

Rosoku emphasizes clarity, reproducibility, and experimental control over maximum automation or throughput.

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🔥 Core Philosophy

Rosoku is designed around a simple idea:

You define what the data are and how they should be processed.
Rosoku defines how experiments are run, evaluated, and recorded.

Rather than enforcing a fixed dataset or model API, Rosoku relies on explicit, callback-driven interfaces that make each experimental choice visible and reproducible.

This makes Rosoku particularly suitable for:

• method development
• ablation studies
• cross-subject / cross-session analysis
• careful comparison of pipelines in research papers

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🧠 What Rosoku Does (and What You Control)

Task	Rosoku handles	You define
Dataset orchestration	train/valid/test grouping	what an item means
Data loading	unified pipeline	how to load (MNE, NumPy, custom)
Preprocessing	execution & split handling	any signal processing you write
Training loop	fitting, scheduling, checkpointing	sklearn estimator / PyTorch model
Evaluation	scoring, grouping, aggregation	metrics, saliency, logging
Result export	parquet / msgpack / pth	downstream analysis or plotting

Rosoku does not:

• impose a dataset format
• hide training logic behind opaque abstractions
• silently modify randomness or preprocessing behavior

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🔧 Two Complementary Pipelines

API	Purpose	Typical models
conventional()	classical ML classification	MDM / TSClassifier / CSP / SVM / LDA
deeplearning()	deep learning with PyTorch	EEGNet / Braindecode / custom CNN/RNN

Both pipelines follow the same design:

1. You define items describing which data belong to each split
2. You provide callbacks to load and preprocess data
3. Rosoku runs training, evaluation, and result aggregation

This shared structure makes it easy to compare classical and deep-learning approaches within the same experimental setup.

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🧪 Reproducibility First

Rosoku is designed with reproducibility as a first-class concern:

• deterministic training is supported via explicit seeding
• data loading behavior is transparent
• no implicit parallelism is used

For maximum reproducibility, Rosoku recommends running with

num_workers = 0

especially when publishing or debugging experiments.

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🚀 Quick Start

Full runnable examples are available under examples/.

Recommended first files:

• examples/example_within-subject-classification-riemannian.py
• examples/example_within-subject-classification-deeplearning.py

These examples demonstrate:

• item-based dataset definition
• grouped test evaluation
• conventional vs deep-learning pipelines
• reproducible experiment execution

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✨ Who Is Rosoku For?

Rosoku is not a black-box AutoML tool.

It is designed for researchers who:

• want to understand and control every step of their pipeline
• need transparent experiments for publications
• work across multiple datasets, subjects, or sessions
• value explicitness over convenience

If you prefer maximum automation, MOABB or Braindecode may be a better fit.
If you want a clear, inspectable bridge between theory and implementation, Rosoku is built for you.