TFTL: A Task-Free Transfer Learning Strategy for EEG-Based Cross-Subject and Cross-Dataset Motor Imagery BCI

• Published in: IEEE transactions on biomedical engineering Vol. 72; no. 2; pp. 810 - 821
• Main Authors: Wang, Yihan, Wang, Jiaxing, Wang, Weiqun, Su, Jianqiang, Bunterngchit, Chayut, Hou, Zeng-Guang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adult; Algorithms; Brain modeling; Brain-Computer Interfaces; Calibration; Clinical trials; cross-dataset; cross-subject; Data models; Datasets; Decoding; Deep Learning; EEG; Electroencephalogram (EEG); Electroencephalography; Electroencephalography - methods; Feature extraction; Female; Human-computer interface; Humans; Imagery; Imagination - physiology; Knowledge management; Machine learning; Male; Mental task performance; motor imagery; Motor skill learning; Performance evaluation; Signal Processing, Computer-Assisted; Statistical analysis; Statistical methods; task-free; Training; Transfer learning; User experience; Young Adult
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2024.3474049

Objective: Motor imagery-based brain-computer interfaces (MI-BCIs) have been playing an increasingly vital role in neural rehabilitation. However, the long-term task-based calibration required for enhanced model performance leads to an unfriendly user experience, while the inadequacy of EEG data hinders the performance of deep learning models. To address these challenges, a task-free transfer learning strategy (TFTL) for EEG-based cross-subject & cross-dataset MI-BCI is proposed for calibration time reduction and multi-center data co-modeling. Methods: TFTL strategy consists of data alignment, shared feature extractor, and specific classifiers, in which the label predictor for MI tasks classification, as well as domain and dataset discriminator for inter-subject variability reduction are concurrently optimized for knowledge transfer from subjects across different datasets to the target subject. Moreover, only resting data of the target subject is used for subject-specific model construction to achieve task-free. Results: We employed three deep learning methods (ShallowConvNet, EEGNet, and TCNet-Fusion) as baseline approaches to evaluate the effectiveness of the proposed strategy on five datasets (BCIC IV Dataset 2a, Dataset 1, Physionet MI, Dreyer 2023, and OpenBMI). The results demonstrate a significant improvement with the inclusion of the TFTL strategy compared to the baseline methods, reaching a maximum enhancement of 15.67% with a statistical significance ( p = 2.4e-5 < 0.05). Moreover, task-free resulted in MI trials needed for calibration being 0 for all datasets, which significantly alleviated the calibration burden for patients before usage. Conclusion/Significance: The proposed TFTL strategy effectively addresses challenges posed by prolonged calibration periods and insufficient EEG data, thus promoting MI-BCI from laboratory to clinical application.