Tutorials!

More tutorials are on the way! In the meantime, check out the EEG2025 Competition lessons and our EEGDash basics guide.

EEG Dash

Playing with eegdash!

EEG P3 Transfer Learning with AS-MMD

EEG P3 Transfer Learning with AS-MMD

Eyes Open vs. Closed Classification

Eyes Open vs. Closed Classification

EEGDash Feature Extractor

EEGDash Feature Extractor

Minimal Tutorial

Minimal Tutorial

Dev scripts for EEGDash

Exploring Braindecode’s BIDSDataset

Exploring Braindecode's BIDSDataset

General tutorials

Age Prediction from EEG

Age Prediction from EEG

Oddball Classification

Oddball Classification

EEG Features for Sex Classification

EEG Features for Sex Classification

Eyes Open vs. Closed Features

Eyes Open vs. Closed Features

P3 Visual Oddball Classification

P3 Visual Oddball Classification

Predicting p-factor from EEG

Predicting p-factor from EEG

P-Factor Regression Tutorial

P-Factor Regression Tutorial

Sex Classification Tutorial

Sex Classification Tutorial

Clinical Dataset Summary

Clinical Dataset Summary

EEGDash API Tutorial

EEGDash API Tutorial

Transfer Learning with EEGDash

Transfer Learning with EEGDash

EEG 2025 Foundation Challenge

1. Cross-Task Transfer Learning: Developing models that can effectively transfer knowledge from passive EEG tasks to active tasks

2. Subject Invariant Representation: Creating robust representations that generalize across different subjects while predicting clinical factors

Challenge 1: Cross-Task Transfer Learning!

Challenge 1: Cross-Task Transfer Learning!

Challenge 2: Predicting the p-factor from EEG

Challenge 2: Predicting the p-factor from EEG

Working Offline with EEGDash

Working Offline with EEGDash

HPC tutorials

Eyes Open vs. Closed Classification

Eyes Open vs. Closed Classification