Calibration-Light Subject-Independent Motor Imagery BCI via Self-Supervised Pretraining and Conformer
DOI:
https://doi.org/10.51903/jtie.v5i1.493Keywords:
motor imagery, EEG, brain–computer interface, subject-independent learningAbstract
Motor imagery (MI) electroencephalography (EEG) is a foundational paradigm for non-invasive brain–computer interfaces (BCIs). However, its practical adoption is constrained by time-consuming per-user calibration and limited cross-subject generalization. This study evaluates a calibration-light MI-BCI framework that combines self-supervised masked EEG pretraining with a lightweight Conformer fine-tuning model. Experiments were conducted on BCI Competition IV Dataset 2b using only the labeled sessions 01T–03T, with artifact-annotated trials removed according to the official 1023 markers. Three deployment-relevant settings were examined: within-subject evaluation (01T–02T → 03T), strict leave-one-subject-out (LOSO) evaluation, and few-shot adaptation with k = 1/5/10 trials per class from the held-out subject’s screening sessions. Full within-subject benchmarking included CSP+LDA, EEGNet, DeepConvNet, ShallowFBCSPNet, supervised Conformer, and SSL+Conformer, while the subject-independent and few-shot analyses focused on CSP+LDA, EEGNet, supervised Conformer, and SSL+Conformer. In the fully calibrated setting, the best mean accuracy was obtained by ShallowFBCSPNet (62.23% ± 14.16%), whereas SSL+Conformer achieved 54.85% ± 11.15% and slightly outperformed the supervised Conformer (53.56% ± 8.81%). Under strict LOSO, EEGNet achieved the highest mean accuracy (52.92% ± 8.25%), while SSL+Conformer reached 51.56% ± 7.18%. In few-shot adaptation, SSL+Conformer achieved the highest mean accuracy at k = 10 (52.84% ± 7.64%) among the core calibration-light methods. The proposed model had a size of 0.1329 MB, a median CPU latency of 0.8777 ms/trial, and LOSO calibration values of ECE = 0.0630 and Brier = 0.4995. These results indicate that masked EEG pretraining provides a competitive lightweight baseline and is most useful when a modest amount of target-subject calibration data is available.
References
Azab, A. M., Mihaylova, L., Ang, K. K., & Arvaneh, M. (2019). Weighted Transfer Learning for Improving Motor Imagery-Based Brain–Computer Interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(7), 1352–1359. https://doi.org/10.1109/tnsre.2019.2923315
Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., & Müller, K.-R. (2008). Optimizing Spatial Filters for Robust EEG Single-Trial Analysis. IEEE Signal Processing Magazine, 25(1), 41–56. https://doi.org/10.1109/msp.2008.4408441
Brier, G. W. (1950). Verification of Forecasts Expressed in Terms of Probability. Monthly Weather Review, 78(1), 1–3. https://doi.org/10.1175/1520-0493(1950)078
Brunner, C., Leeb, R., Müller-Putz, G. R., Schlögl, A., & Pfurtscheller, G. (2008). BCI Competition 2008 – Graz Data Set B. Graz University of Technology. https://bbci.de/competition/iv/desc_2b.pdf
Cai, M., & Zeng, Y. (2024). MAE-EEG-Transformer: A Transformer-Based Approach Combining Masked Autoencoder and Cross-Individual Data Augmentation Pre-Training for EEG Classification. Biomedical Signal Processing and Control, 94, 106131. https://doi.org/10.1016/j.bspc.2024.106131
Delorme, A., & Makeig, S. (2004). EEGLAB: An Open Source Toolbox for Analysis of Single-Trial EEG Dynamics Including Independent Component Analysis. Journal of Neuroscience Methods, 134(1), 9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009
Fu, Z., Zhu, H., Zhao, Y., Huan, R., Zhang, Y., Chen, S., & Pan, Y. (2024). GMAEEG: A Self-Supervised Graph Masked Autoencoder for EEG Representation Learning. IEEE Journal of Biomedical and Health Informatics, 28(11), 6486–6497. https://doi.org/10.1109/jbhi.2024.3443651
Gulati, A., Qin, J., Chiu, C.-C., Parmar, N., Zhang, Y., Yu, J., Han, W., Wang, S., Zhang, Z., & Wu, Y. (2020). Conformer: Convolution-Augmented Transformer for Speech Recognition. Proceedings of Interspeech 2020, 5036–5040. https://doi.org/10.21437/interspeech.2020-3015
Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On Calibration of Modern Neural Networks. Proceedings of the 34th International Conference on Machine Learning (ICML), 1321–1330. https://proceedings.mlr.press/v70/guo17a.html
Handoko, M., Parancika, R. B., Aris, M., & Ardi, Y. M. (2025). Determination of Employee Performance: Work Environment and Leadership Style (Case Study at PT MPIW Jakarta). Journal of Management and Informatics (JMI), 4(2), 773–790. https://doi.org/10.51903/jmi.v4i2.216
He, H., & Wu, D. (2020). Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach. IEEE Transactions on Biomedical Engineering, 67(2), 399–410. https://doi.org/10.1109/tbme.2019.2913914
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., & Girshick, R. (2022). Masked Autoencoders Are Scalable Vision Learners. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 16000–16009. https://doi.org/10.1109/cvpr52688.2022.01574
Hendry, H., & Manongga, D. (2024). Implementation of Multi-Node Sensor Data Delivery Using the Master-Slave Method in LoRa Communication. Journal of Technology Informatics and Engineering (JTIE), 3(2), 117–137. https://jtie.stekom.ac.id/index.php/jtie/article/view/279
Juan, J. V., Martínez, R., Iáñez, E., Ortiz, M., Tornero, J., & Azorín, J. M. (2024). Exploring EEG-Based Motor Imagery Decoding: A Dual Approach Using Spatial Features and Spectro-Spatial Deep Learning Model IFNet. Frontiers in Neuroinformatics, 18, 1345425. https://doi.org/10.3389/fninf.2024.1345425
Kingma, D. P., & Ba, J. (2015). Adam: A Method for Stochastic Optimization. International Conference on Learning Representations (ICLR). https://arxiv.org/abs/1412.6980
Ko, W., Jeon, E., Jeong, S., Phyo, J., & Suk, H.-I. (2021). A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain–Computer Interfaces. Frontiers in Human Neuroscience, 15, 643386. https://doi.org/10.3389/fnhum.2021.643386
Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018). EEGNet: A Compact Convolutional Neural Network for EEG-Based Brain–Computer Interfaces. Journal of Neural Engineering, 15(5), 056013. https://doi.org/10.1088/1741-2552/aace8c
Leeb, R., Brunner, C., Müller-Putz, G. R., Schlögl, A., & Pfurtscheller, G. (2008). BCI Competition 2008 – Graz Data Set B (BCI Competition IV Dataset 2b): Description. Graz University of Technology. https://bbci.de/competition/iv/desc_2b.pdf
Li, M., & Xu, D. (2024). Transfer Learning in Motor Imagery Brain Computer Interface: A Review. Journal of Shanghai Jiaotong University (Science), 29, 37–59. https://doi.org/10.1007/s12204-022-2488-4
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007). A Review of Classification Algorithms for EEG-Based Brain–Computer Interfaces. Journal of Neural Engineering, 4(2), 1–13. https://doi.org/10.1088/1741-2560/4/2/r01
Müller-Putz, G. R., Scherer, R., Brunner, C., Leeb, R., & Pfurtscheller, G. (2008). Better Than Random: A Closer Look on BCI Results. International Journal of Bioelectromagnetism, 10(1), 52–55. http://www.ijbem.org/volume10/number1/papers/paper7.pdf
Oppenheim, A. V., & Schafer, R. W. (2009). Discrete-Time Signal Processing (3rd ed.). Pearson. https://www.pearson.com/en-us/subject-catalog/p/discrete-time-signal-processing/P200000003144
Pfurtscheller, G., & Neuper, C. (2001). Motor Imagery and Direct Brain–Computer Communication. Proceedings of the IEEE, 89(7), 1123–1134. https://doi.org/10.1109/5.939829
Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., & Ball, T. (2017). Deep Learning with Convolutional Neural Networks for EEG Decoding and Visualization. Human Brain Mapping, 38(11), 5391–5420. https://doi.org/10.1002/hbm.23730
She, Q., Chen, T., Fang, F., Zhang, J., Gao, Y., & Zhang, Y. (2023). Improved Domain Adaptation Network Based on Wasserstein Distance for Motor Imagery EEG Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 1137–1148. https://doi.org/10.1109/tnsre.2023.3241846
Tangermann, M., Müller, K.-R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., Leeb, R., Mehring, C., Miller, K. J., Müller-Putz, G., Nolte, G., Pfurtscheller, G., Preissl, H., Schalk, G., Schlögl, A., Vidaurre, C., Waldert, S., & Blankertz, B. (2012). Review of the BCI Competition IV. Frontiers in Neuroscience, 6, 55. https://doi.org/10.3389/fnins.2012.00055
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 5998–6008. https://arxiv.org/abs/1706.03762
Wang, X., Liesaputra, V., Liu, Z., Wang, Y., & Huang, Z. (2024). An In-Depth Survey on Deep Learning-Based Motor Imagery Electroencephalogram (EEG) Classification. Artificial Intelligence in Medicine, 147, 102738. https://doi.org/10.1016/j.artmed.2023.102738
Wimpff, M., Gizzi, L., Zerfowski, J., & Yang, B. (2024). EEG Motor Imagery Decoding: A Framework for Comparative Analysis With Channel Attention Mechanisms. Journal of Neural Engineering, 21(3), 036020. https://doi.org/10.1088/1741-2552/ad48b9
Zainudin, A., Hadi, A. P., & Priyadi, A. (2024). Sistem Informasi Persediaan Obat Berbasis Web di Rumah Sakit Bina Kasih. JUISI: Jurnal Ilmiah Sistem Informasi, 3(3), 30–34. https://doi.org/10.51903/776j7727
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Qiyou Wu, Gaotian Mi, Dan Wood
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
