# Is Limited Participant Diversity Impeding EEG-based Machine Learning?

arXiv: https://arxiv.org/abs/2503.13497 · HTML: https://mlsanity.com/paper/2503.13497/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 63 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 20 of 20)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024)
- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [The Optimal Sample Complexity of PAC Learning](https://mlsanity.com/paper/1507.00473/) (2015)
- [MAEEG: Masked Auto-encoder for EEG Representation Learning](https://mlsanity.com/paper/2211.02625/) (2022)
- [EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model](https://mlsanity.com/paper/2401.10278/) (2024)
- [ICLabel: An automated electroencephalographic independent component classifier, dataset, and website](https://mlsanity.com/paper/1901.07915/) (2019)
- [MAtt: A Manifold Attention Network for EEG Decoding](https://mlsanity.com/paper/2210.01986/) (2022)
- [Machine-Learning-Based Diagnostics of EEG Pathology](https://mlsanity.com/paper/2002.05115/) (2020)
- [Data augmentation for learning predictive models on EEG: a systematic comparison](https://mlsanity.com/paper/2206.14483/) (2022)
- [CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals](https://mlsanity.com/paper/2106.13695/) (2021)
- [Scaling laws for decoding images from brain activity](https://mlsanity.com/paper/2501.15322/) (2025)
- [Revisiting Agnostic PAC Learning](https://mlsanity.com/paper/2407.19777/) (2024)
- [Addressing Class Imbalance in Classification Problems of Noisy Signals by using Fourier Transform Surrogates](https://mlsanity.com/paper/1806.08675/) (2018)
- [On the Limitations of General Purpose Domain Generalisation Methods](https://mlsanity.com/paper/2202.00563/) (2022)
- [Toward Generalizing Visual Brain Decoding to Unseen Subjects](https://mlsanity.com/paper/2410.14445/) (2024)
- [SPEED: Scalable Preprocessing of EEG Data for Self-Supervised Learning](https://mlsanity.com/paper/2408.08065/) (2024)

## Built on by (most cited first, 6 of 6)

- [EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding](https://mlsanity.com/paper/2506.19141/) (2025)
- [DIVER-1: Scaling Intracranial EEG Foundation Models for Transferable Representations](https://mlsanity.com/paper/2512.19097/) (2025)
- [Mitigating Subject Dependency in EEG Decoding with Subject-Specific Low-Rank Adapters](https://mlsanity.com/paper/2510.08059/) (2025)
- [A Multi-dimensional Framework for Evaluating Generalization in EEG Foundation Models](https://mlsanity.com/paper/2605.28563/) (2026)
- [Informed Bootstrap Augmentation Improves EEG Decoding](https://mlsanity.com/paper/2511.12073/) (2025)
- [OOD Detection for EEG-based Machine Learning in High-Risk Environments](https://mlsanity.com/paper/2608.17620/) (2026)

## Abstract (arXiv)

The application of machine learning (ML) to electroencephalography (EEG) has great potential to advance both neuroscientific research and clinical applications. However, the generalisability and robustness of EEG-based ML models often hinge on the amount and diversity of training data. It is common practice to split EEG recordings into small segments, thereby increasing the number of samples substantially compared to the number of individual recordings or participants. We conceptualise this as a multi-level data generation process and investigate the scaling behaviour of model performance with respect to the overall sample size and the participant diversity through large-scale empirical studies. We then use the same framework to investigate the effectiveness of different ML strategies designed to address limited data problems: data augmentations and self-supervised learning. Our findings show that model performance scaling can be severely constrained by participant distribution shifts and provide actionable guidance for data collection and ML research. The code for our experiments is publicly available online.