mlsanityData to 14 Oct 2026

Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces

arXiv 2502.09203 ↗First posted 13 Feb 2025cs.HC cs.LGcited by 7

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Abstract (arXiv)

Due to large intra-subject and inter-subject variabilities of electroencephalogram (EEG) signals, EEG-based brain-computer interfaces (BCIs) usually need subject-specific calibration to tailor the decoding algorithm for each new subject, which is time-consuming and user-unfriendly, hindering their real-world applications. Transfer learning (TL) has been extensively used to expedite the calibration, by making use of EEG data from other subjects/sessions. An important consideration in TL for EEG-based BCIs is to reduce the data distribution discrepancies among different subjects/sessions, to avoid negative transfer. Euclidean alignment (EA) was proposed in 2020 to address this challenge. Numerous experiments from 13 different BCI paradigms demonstrated its effectiveness and efficiency. This paper revisits EA, explaining its procedure and correct usage, introducing its applications and extensions, and pointing out potential new research directions. It should be very helpful to BCI researchers, especially those who are working on EEG signal decoding.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 35 of its 116 references that are in the map. First posted on arXiv 13 Feb 2025.

Builds on

35 of 116 references.

References · 35

  1. 2019A Comprehensive Survey on Transfer Learningcited by 1,160
  2. 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 628
  3. 2015Return of Frustratingly Easy Domain Adaptationcited by 391
  4. 2024Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCIcited by 327
  5. 2020A Survey on Negative Transfercited by 122
  6. 2018Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approachcited by 76
  7. 2020Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016cited by 59
  8. 2020Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipelinecited by 31
  9. 20222021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Setscited by 23
  10. 2018Parallel Transport on the Cone Manifold of SPD Matrices for Domain Adaptationcited by 20
  11. 2019On the Vulnerability of CNN Classifiers in EEG-Based BCIscited by 17
  12. 2020Tiny noise, big mistakes: Adversarial perturbations induce errors in Brain-Computer Interface spellerscited by 15
23 more
  1. 2019Protecting Privacy of Users in Brain-Computer Interface Applicationscited by 13
  2. 2019White-Box Target Attack for EEG-Based BCI Regression Problemscited by 13
  3. 2020EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attackscited by 13
  4. 2023Calibration-free online test-time adaptation for electroencephalography motor imagery decodingcited by 12
  5. 2024A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decodingcited by 12
  6. 2024SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEGcited by 12
  7. 2019Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approachcited by 11
  8. 2024Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignmentcited by 11
  9. 2017Online and Offline Domain Adaptation for Reducing BCI Calibration Effortcited by 10
  10. 2019Manifold Embedded Knowledge Transfer for Brain-Computer Interfacescited by 10
  11. 2017Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)cited by 9
  12. 2019EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Trainingcited by 9
  13. 2021Adversarial Attacks and Defenses in Physiological Computing: A Systematic Reviewcited by 8
  14. 2022Team Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competitioncited by 7
  15. 2024Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfacescited by 7
  16. 2024Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIscited by 6
  17. 2024User-wise Perturbations for User Identity Protection in EEG-Based BCIscited by 5
  18. 2024Privacy-Preserving Brain-Computer Interfaces: A Systematic Reviewcited by 5
  19. 2017Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)cited by 4
  20. 2025Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding: Insights from a Large-Scale Longitudinal Studycited by 4
  21. 2017EEG-Based User Reaction Time Estimation Using Riemannian Geometry Featurescited by 3
  22. 2024Protecting Multiple Types of Privacy Simultaneously in EEG-based Brain-Computer Interfacescited by 3
  23. 2024PAT: Privacy-Preserving Adversarial Transfer for Accurate, Robust and Privacy-Preserving EEG Decodingcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.

  1. 2025Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classificationcited by 7
  2. 2025MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery Classificationcited by 5
  3. 2026SAFE: Secure and Accurate Federated Learning for Privacy-Preserving Brain-Computer Interfacescited by 2
  4. 2025Spiking Neural Network for Intra-cortical Brain Signal Decodingcited by 0
  5. 2025FusionGen: Feature Fusion-Based Few-Shot EEG Data Generationcited by 0
  6. 2026Rethinking Self-Training Based Cross-Subject Domain Adaptation for SSVEP Classificationcited by 0
  7. 2026NEXUS-MI: Communication-Aware Federated Personalization for Gateway-Coordinated Motor-Imagery Brain-Computer Interfacescited by 0