# EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification

arXiv: https://arxiv.org/abs/2311.05415 · HTML: https://mlsanity.com/paper/2311.05415/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2023.

## Built on by (most cited first, 9 of 9)

- [Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods](https://mlsanity.com/paper/2604.27033/) (2026)
- [PL-DCP: A Pairwise Learning framework with Domain and Class Prototypes for EEG emotion recognition under unseen target conditions](https://mlsanity.com/paper/2412.00082/) (2024)
- [Learning Domain- and Class-Disentangled Prototypes for Domain-Generalized EEG Emotion Recognition](https://mlsanity.com/paper/2509.01135/) (2025)
- [Rethinking Generalized BCIs: Benchmarking 340,000+ Unique Algorithmic Configurations for EEG Mental Command Decoding](https://mlsanity.com/paper/2512.02978/) (2025)
- [A swap-adversarial framework for improving domain generalization in electrocorticography-based Parkinson's disease classification](https://mlsanity.com/paper/2602.10528/) (2026)
- [Average Rankings Mask Per-Subject Optimality: A Friedman-Nemenyi Benchmark of EEG Motor-Imagery BCI Decoders](https://mlsanity.com/paper/2606.24394/) (2026)
- [Subject-Level Heterogeneity in EEG Motor Imagery Decoding: A Large-Scale Benchmark and Portfolio-Based Reduction of the Search Space](https://mlsanity.com/paper/2607.22778/) (2026)
- [ATCNet-CIAM for Multi-Session Motor Imagery EEG Signal Classification](https://mlsanity.com/paper/2607.23522/) (2026)
- [EEG-Fusion: Failure-Informed Source-Free Expert Routing for Robust Motor Imagery EEG Decoding](https://mlsanity.com/paper/2609.33962/) (2026)

## Abstract (arXiv)

Motor imagery EEG classification plays a crucial role in non-invasive Brain-Computer Interface (BCI) research. However, the classification is affected by the non-stationarity and individual variations of EEG signals. Simply pooling EEG data with different statistical distributions to train a classification model can severely degrade the generalization performance. To address this issue, the existing methods primarily focus on domain adaptation, which requires access to the target data during training. This is unrealistic in many EEG application scenarios. In this paper, we propose a novel multi-source domain generalization framework called EEG-DG, which leverages multiple source domains with different statistical distributions to build generalizable models on unseen target EEG data. We optimize both the marginal and conditional distributions to ensure the stability of the joint distribution across source domains and extend it to a multi-source domain generalization framework to achieve domain-invariant feature representation, thereby alleviating calibration efforts. Systematic experiments on a simulative dataset and BCI competition datasets IV-2a and IV-2b demonstrate the superiority of our proposed EEG-DG over state-of-the-art methods. Specifically, EEG-DG achieves an average classification accuracy/kappa value of 81.79%/0.7572 and 87.12%/0.7424 on datasets IV-2a and IV-2b, respectively, which even outperforms some domain adaptation methods. Our code is available at https://github.com/XC-ZhongHIT/EEG-DG for free download and evaluation.