Concepts#

These pages explain why, not how. They describe the ideas, design choices, and tradeoffs behind EEGDash so that you can reason about your own pipeline, debug surprising results, and read the rest of the documentation with the right mental model. For step-by-step recipes, see the Tutorials and the How-To guides.

The split between “explanation”, “tutorials”, “how-to guides”, and “reference” follows the Diataxis framework. Each part of the documentation answers a different question:

  • Tutorials answer learning questions. They are guided lessons.

  • How-to guides answer task questions. They are recipes.

  • Reference answers information questions. It is exhaustive lookup.

  • Concepts (this section) answer understanding questions. They explain the model, not the keystrokes.

When you find yourself wanting to know “but why does it work this way?”, a concept page is the right place to look. When a tutorial says “we use a subject-aware split here”, the underlying argument lives in Leakage and evaluation. When a how-to says “set a montage”, the reasoning is in Preprocessing decisions.

Available concept pages#

Summary of each page:

  • EEGDash objects: EEGDash, EEGDashDataset, EEGChallengeDataset — The three main objects (EEGDash, EEGDashDataset, EEGChallengeDataset), what each one returns, and when to reach for which.

  • Metadata and BIDS entities — How BIDS entities (subject, session, task, run) map to EEGDash query keywords, why standardized metadata matters, and how participant-level descriptors flow through the dataset.

  • Leakage and evaluation — Why subject-level data leakage destroys generalization claims in EEG decoding, why random window splits are unsafe, and how within-subject, cross-session, and cross-subject evaluation differ.

  • Preprocessing decisions — What changes when you pick a high-pass cutoff, a montage, or a reference scheme. Defaults are not neutral choices: they encode assumptions about the signal and the question.

  • Features vs. deep learning — When handcrafted features (band power, CSP, Riemannian) outperform deep nets and vice versa. How to pick a baseline that is informative, not just easy.

How to use these pages#

Read a concept page before you start a project so you know which questions to ask. Re-read it after something surprising happens — a suspiciously high accuracy, a result that disappears across subjects, a filter that changes class boundaries — so you can localise the assumption that broke. The pages are deliberately short on syntax. The tutorials contain runnable code; the how-to guides contain task recipes; this section contains the reasoning that connects them.

Further reading#