# 2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets

arXiv: https://arxiv.org/abs/2202.12950 · HTML: https://mlsanity.com/paper/2202.12950/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 49 references that are in the map.
- First posted on arXiv 14 Feb 2022.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [What makes ImageNet good for transfer learning?](https://mlsanity.com/paper/1608.08614/) (2016)
- [A Riemannian Network for SPD Matrix Learning](https://mlsanity.com/paper/1608.04233/) (2016)
- [Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach](https://mlsanity.com/paper/1808.05464/) (2018)
- [Riemannian batch normalization for SPD neural networks](https://mlsanity.com/paper/1909.02414/) (2019)
- [Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016](https://mlsanity.com/paper/2004.06286/) (2020)
- [A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series](https://mlsanity.com/paper/1707.03321/) (2017)
- [Transfer Learning in Brain-Computer Interfaces](https://mlsanity.com/paper/1512.00296/) (2015)
- [Inter-subject Deep Transfer Learning for Motor Imagery EEG Decoding](https://mlsanity.com/paper/2103.05351/) (2021)
- [Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach](https://mlsanity.com/paper/1912.01166/) (2019)
- [Team Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competition](https://mlsanity.com/paper/2202.03267/) (2022)

## Built on by (most cited first, 23 of 23)

- [SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG](https://mlsanity.com/paper/2206.01323/) (2022)
- [The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark](https://mlsanity.com/paper/2404.15319/) (2024)
- [EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models](https://mlsanity.com/paper/2508.17742/) (2025)
- [NeuralBench: A Unifying Framework to Benchmark NeuroAI Models](https://mlsanity.com/paper/2605.08495/) (2026)
- [A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding](https://mlsanity.com/paper/2401.10746/) (2024)
- [SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEG](https://mlsanity.com/paper/2411.07249/) (2024)
- [EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding](https://mlsanity.com/paper/2506.19141/) (2025)
- [Evaluating the structure of cognitive tasks with transfer learning](https://mlsanity.com/paper/2308.02408/) (2023)
- [Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2502.09203/) (2025)
- [Latent Alignment with Deep Set EEG Decoders](https://mlsanity.com/paper/2311.17968/) (2023)
- [Transfer Learning between Motor Imagery Datasets using Deep Learning -- Validation of Framework and Comparison of Datasets](https://mlsanity.com/paper/2311.16109/) (2023)
- [Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities](https://mlsanity.com/paper/2211.02624/) (2022)
- [Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets](https://mlsanity.com/paper/2403.15415/) (2024)
- [Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations](https://mlsanity.com/paper/2405.19345/) (2024)
- [HEEGNet: Hyperbolic Embeddings for EEG](https://mlsanity.com/paper/2601.03322/) (2026)
- [Advancing Brainwave Modeling with a Codebook-Based Foundation Model](https://mlsanity.com/paper/2505.16724/) (2025)
- [Zero-Shot Neural Priors for Generalizable Cross-Subject and Cross-Task EEG Decoding](https://mlsanity.com/paper/2606.23706/) (2026)
- [Federated deep transfer learning for EEG decoding using multiple BCI tasks](https://mlsanity.com/paper/2211.10976/) (2022)
- [Convolutional Monge Mapping Normalization for learning on sleep data](https://mlsanity.com/paper/2305.18831/) (2023)
- [EEG-D3: A Solution to the Hidden Overfitting Problem of Deep Learning Models](https://mlsanity.com/paper/2512.13806/) (2025)
- [A Composable Channel-Adaptive Architecture for Seizure Classification](https://mlsanity.com/paper/2512.19123/) (2025)
- [Rectifying Geometric Misalignment: Online Source-Free Adaptation for Class-Imbalanced EEG](https://mlsanity.com/paper/2608.05315/) (2026)
- [SPERA: Spherical Prior EEG Foundation Model with Geometry- and Frequency-Aware Latent Prediction](https://mlsanity.com/paper/2610.10571/) (2026)

## Abstract (arXiv)

Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is because current methods cannot generalise well across human subjects' data and handle learning from different heterogeneously collected data sets, thus limiting the scale of training data. On the other side, developments in transfer learning would benefit significantly from a real-world benchmark with immediate practical application. Therefore, we pick electroencephalography (EEG) as an exemplar for what makes biosignal machine learning hard. We design two transfer learning challenges around diagnostics and Brain-Computer-Interfacing (BCI), that have to be solved in the face of low signal-to-noise ratios, major variability among subjects, differences in the data recording sessions and techniques, and even between the specific BCI tasks recorded in the dataset. Task 1 is centred on the field of medical diagnostics, addressing automatic sleep stage annotation across subjects. Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets. The BEETL competition with its over 30 competing teams and its 3 winning entries brought attention to the potential of deep transfer learning and combinations of set theory and conventional machine learning techniques to overcome the challenges. The results set a new state-of-the-art for the real-world BEETL benchmark.