# Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach

arXiv: https://arxiv.org/abs/1808.05464 · HTML: https://mlsanity.com/paper/1808.05464/

## Key facts

- Cited by 76 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2018.

## Built on by (most cited first, 30 of 76)

- [REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects](https://mlsanity.com/paper/2510.21585/) (2025)
- [SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG](https://mlsanity.com/paper/2206.01323/) (2022)
- [Artificial Intelligence for the Metaverse: A Survey](https://mlsanity.com/paper/2202.10336/) (2022)
- [2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets](https://mlsanity.com/paper/2202.12950/) (2022)
- [T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs](https://mlsanity.com/paper/2412.07228/) (2024)
- [Calibration-free online test-time adaptation for electroencephalography motor imagery decoding](https://mlsanity.com/paper/2311.18520/) (2023)
- [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)
- [Toward Open-World Electroencephalogram Decoding Via Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2112.06654/) (2021)
- [Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment](https://mlsanity.com/paper/2412.17842/) (2024)
- [DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding](https://mlsanity.com/paper/2506.21140/) (2025)
- [Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review](https://mlsanity.com/paper/2102.02729/) (2021)
- [On statistic alignment for domain adaptation in structural health monitoring](https://mlsanity.com/paper/2205.12052/) (2022)
- [Team Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competition](https://mlsanity.com/paper/2202.03267/) (2022)
- [Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework](https://mlsanity.com/paper/2202.09559/) (2022)
- [Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces](https://mlsanity.com/paper/2412.01079/) (2024)
- [Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2502.09203/) (2025)
- [Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification](https://mlsanity.com/paper/2503.05349/) (2025)
- [Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment](https://mlsanity.com/paper/2407.14303/) (2024)
- [Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs](https://mlsanity.com/paper/2411.02094/) (2024)
- [Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training](https://mlsanity.com/paper/2211.11460/) (2022)
- [A Strong and Simple Deep Learning Baseline for BCI MI Decoding](https://mlsanity.com/paper/2309.07159/) (2023)
- [Latent Alignment with Deep Set EEG Decoders](https://mlsanity.com/paper/2311.17968/) (2023)
- [Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2412.09006/) (2024)
- [MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery Classification](https://mlsanity.com/paper/2507.20254/) (2025)
- [Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding: Insights from a Large-Scale Longitudinal Study](https://mlsanity.com/paper/2502.06828/) (2025)
- [Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities](https://mlsanity.com/paper/2211.02624/) (2022)
- [Unsupervised Adaptive Deep Learning Method For BCI Motor Imagery Decoding](https://mlsanity.com/paper/2403.15438/) (2024)
- [PAT: Privacy-Preserving Adversarial Transfer for Accurate, Robust and Privacy-Preserving EEG Decoding](https://mlsanity.com/paper/2412.11390/) (2024)
- [MVCNet: Multi-View Contrastive Network for Motor Imagery Classification](https://mlsanity.com/paper/2502.17482/) (2025)

## Abstract (arXiv)

Objective: This paper targets a major challenge in developing practical EEG-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data? Methods: We propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: 1) it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction and machine learning algorithms can then be applied to the aligned trials; 2) its computational cost is very low; and, 3) it is unsupervised and does not need any label information from the new subject. Results: Both offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment. Conclusion: The proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs. Significance: Our proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs.