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Estimated reading time:3 minutes

Three short, CPU-only lessons that take you from a fresh pip install eegdash to a working PyTorch DataLoader over real BIDS-curated EEG records. Each tutorial runs in under a few minutes, executes on the docs CI matrix on every build, and has a matching YAML spec under docs/tutorials/_spec/.

This category is the gateway to every other section in this gallery. The core decoding workflow assumes you have already loaded a recording; the features and evaluation tracks assume you understand windowing; the transfer / foundation track assumes you can wire an EEGDashDataset into a dataloader. Sourced from docs/tutorial_restructure_plan.md Category A (lines 360-380), with BIDS metadata handling per Pernet et al. (2019).

What you will learn:

How to query the EEGDash index for datasets and records without downloading raw signals.
How to load one BIDS recording, inspect its sampling rate, channels, and events, and verify it before scaling up.
How to apply Braindecode-safe preprocessors and convert continuous signal into fixed-length windows.
How to wrap an EEGDashDataset in a torch.utils.data.DataLoader and confirm one batch’s shape.
The vocabulary – BIDS entities, record vs dataset documents, windows – you’ll see in every later tutorial.

Run the lessons in order:

plot_00_first_search.py – find datasets and records.
plot_01_first_recording.py – load one recording and inspect it.
plot_02_dataset_to_dataloader.py – build windows and a PyTorch DataLoader.

How do I find datasets in EEGDash?

How do I load one EEG recording from EEGDash?

How do I turn one EEG recording into a PyTorch DataLoader?