# Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach

arXiv: https://arxiv.org/abs/1912.01166 · HTML: https://mlsanity.com/paper/1912.01166/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 11 of 11)

- [2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets](https://mlsanity.com/paper/2202.12950/) (2022)
- [Toward Open-World Electroencephalogram Decoding Via Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2112.06654/) (2021)
- [Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2502.09203/) (2025)
- [Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs](https://mlsanity.com/paper/2411.02094/) (2024)
- [Domain Adaptation and Generalization on Functional Medical Images: A Systematic Survey](https://mlsanity.com/paper/2212.03176/) (2022)
- [Do Deep Neural Networks Always Perform Better When Eating More Data?](https://mlsanity.com/paper/2205.15187/) (2022)
- [Cross-tokamak Disruption Prediction based on Physics-Guided Feature Extraction and domain adaptation](https://mlsanity.com/paper/2309.05361/) (2023)
- [Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imagery](https://mlsanity.com/paper/2408.01163/) (2024)
- [Tailoring deep learning for real-time brain-computer interfaces: From offline models to calibration-free online decoding](https://mlsanity.com/paper/2507.06779/) (2025)
- [EEG classifier cross-task transfer to avoid training sessions in robot-assisted rehabilitation](https://mlsanity.com/paper/2402.17790/) (2024)
- [EDAPT: Towards Calibration-Free BCIs with Continual Online Adaptation](https://mlsanity.com/paper/2508.10474/) (2025)

## Abstract (arXiv)

A brain-computer interface (BCI) system usually needs a long calibration session for each new subject/task to adjust its parameters, which impedes its transition from the laboratory to real-world applications. Domain adaptation, which leverages labeled data from auxiliary subjects/tasks (source domains), has demonstrated its effectiveness in reducing such calibration effort. Currently, most domain adaptation approaches require the source domains to have the same feature space and label space as the target domain, which limits their applications, as the auxiliary data may have different feature spaces and/or different label spaces. This paper considers different set domain adaptation for BCIs, i.e., the source and target domains have different label spaces. We introduce a practical setting of different label sets for BCIs, and propose a novel label alignment (LA) approach to align the source label space with the target label space. It has three desirable properties: 1) LA only needs as few as one labeled sample from each class of the target subject; 2) LA can be used as a preprocessing step before different feature extraction and classification algorithms; and, 3) LA can be integrated with other domain adaptation approaches to achieve even better performance. Experiments on two motor imagery datasets demonstrated the effectiveness of LA.