# Towards physiology-informed data augmentation for EEG-based BCIs

arXiv: https://arxiv.org/abs/2203.14392 · HTML: https://mlsanity.com/paper/2203.14392/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 27 Mar 2022.

## Builds on (most cited first, 7 of 7)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals](https://mlsanity.com/paper/1806.01875/) (2018)
- [Transfer Learning in Brain-Computer Interfaces](https://mlsanity.com/paper/1512.00296/) (2015)
- [Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification](https://mlsanity.com/paper/1901.07429/) (2019)
- [Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders](https://mlsanity.com/paper/1812.06857/) (2018)
- [Online and Offline Domain Adaptation for Reducing BCI Calibration Effort](https://mlsanity.com/paper/1702.02897/) (2017)
- [Active Semi-supervised Transfer Learning (ASTL) for Offline BCI Calibration](https://mlsanity.com/paper/1805.05781/) (2018)

## Built on by (most cited first, 4 of 4)

- [Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations](https://mlsanity.com/paper/2405.19345/) (2024)
- [System Filter-Based Common Components Modeling for Cross-Subject EEG Decoding](https://mlsanity.com/paper/2507.05268/) (2025)
- [A New Strategy for Artificial Intelligence: Training Foundation Models Directly on Human Brain Data](https://mlsanity.com/paper/2601.12053/) (2026)
- [A Comprehensive Inference-Time Augmentation Framework in Physiological Signals: Application to PPG-Based AF Detection](https://mlsanity.com/paper/2606.10410/) (2026)

## Abstract (arXiv)

Most EEG-based Brain-Computer Interfaces (BCIs) require a considerable amount of training data to calibrate the classification model, owing to the high variability in the EEG data, which manifests itself between participants, but also within participants from session to session (and, of course, from trial to trial). In general, the more complex the model, the more data for training is needed. We suggest a novel technique for augmenting the training data by generating new data from the data set at hand. Different from existing techniques, our method uses backward and forward projection using source localization and a head model to modify the current source dipoles of the model, thereby generating inter-participant variability in a physiologically meaningful way. In this manuscript, we explain the method and show first preliminary results for participant-independent motor-imagery classification. The accuracy was increased when using the proposed method of data augmentation by 13, 6 and 2 percentage points when using a deep neural network, a shallow neural network and LDA, respectively.