mlsanityData to 7 Oct 2026

A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface

arXiv 2209.03785 ↗First posted 7 Sep 2022eess.SP cs.AI cs.HCcited by 6

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Abstract (arXiv)

Brain-computer interface (BCI) provides a direct communication pathway between human brain and external devices. Before a new subject could use BCI, a calibration procedure is usually required. Because the inter- and intra-subject variances are so large that the models trained by the existing subjects perform poorly on new subjects. Therefore, effective subject-transfer and calibration method is essential. In this paper, we propose a semi-supervised meta learning (SSML) method for subject-transfer learning in BCIs. The proposed SSML learns a meta model with the existing subjects first, then fine-tunes the model in a semi-supervised learning manner, i.e. using few labeled and many unlabeled samples of target subject for calibration. It is significant for BCI applications where the labeled data are scarce or expensive while unlabeled data are readily available. To verify the SSML method, three different BCI paradigms are tested: 1) event-related potential detection; 2) emotion recognition; and 3) sleep staging. The SSML achieved significant improvements of over 15% on the first two paradigms and 4.9% on the third. The experimental results demonstrated the effectiveness and potential of the SSML method in BCI applications.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 15 of its 60 references that are in the map. First posted on arXiv 7 Sep 2022.

Builds on

15 of 60 references.

References · 15

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  2. 2020Meta-Learning in Neural Networks: A Surveycited by 944
  3. 2021Time-Series Representation Learning via Temporal and Contextual Contrastingcited by 342
  4. 2017DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEGcited by 154
  5. 2019EEG-Based Emotion Recognition Using Regularized Graph Neural Networkscited by 97
  6. 2020Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016cited by 56
  7. 2018Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classificationcited by 42
  8. 2019A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognitioncited by 42
  9. 2022PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognitioncited by 12
  10. 2020Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classificationcited by 10
  11. 2021CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNetcited by 7
  12. 2020Ultra Efficient Transfer Learning with Meta Update for Cross Subject EEG Classificationcited by 5
3 more
  1. 2020Few-shot Decoding of Brain Activation Mapscited by 5
  2. 2018Diffuse to fuse EEG spectra -- intrinsic geometry of sleep dynamics for classificationcited by 4
  3. 2021Model-Agnostic Meta-Learning for EEG Motor Imagery Decoding in Brain-Computer-Interfacingcited by 4

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Cited by year

Citing papers per year: 2023: 2; 2024: 3; 2025: 1.

6 papers.

  1. 2023Evaluating the structure of cognitive tasks with transfer learningcited by 8
  2. 2024A Survey of Few-Shot Learning for Biomedical Time Seriescited by 7
  3. 2024Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention Mechanismscited by 4
  4. 2023Accurate deep learning sub-grid scale models for large eddy simulationscited by 1
  5. 2024Evaluating Fast Adaptability of Neural Networks for Brain-Computer Interfacecited by 0
  6. 2025FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognitioncited by 0