Abstract
Motor imagery (MI) electroencephalogram (EEG) decoding, as a core component widely used in noninvasive brain-computer interface (BCI) system, is critical to realize the interaction purpose of physical world and brain activity. However, the conventional methods are challenging to obtain desirable results for two main reasons: there is a small amount of labeled data making it difficult to fully exploit the features of EEG signals, and lack of unified expert knowledge among different individuals. To handle these dilemmas, a novel small-sample EE -G decoding method based on abductive learning (SSE-ABL) is proposed in this paper, which integrates perceiving module that can extract multiscale features of multi-channel EEG in semantic level and knowledge base module of brain science. The former module is trained via pseudo-labels of unlabeled EEG signals generated by abductive learning, and the latter is refined via the label distribution predicted by semi-supervised learning. Experimental results demonstrate that SSE-ABL has a superior performance compared with state-of-the-art methods and is also convenient for visualizing the underlying information flow of EEG decoding.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, D.L., Xu, J.C., Wang, J.H., Fang, X.K., Ji, Y.: A multi-scale fusion convolutional neural network based on attention mechanism for the visualization analysis of EEG signals decoding. IEEE Trans. Neur. Sys. Rehabil. Eng. 28(12), 2615–2626 (2020)
Zhang, X., Yao, L.N, Wang, X.Z., et al.: A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. J. Neur. Eng. 18(3), 031002 (2021)
Valizadeh, S.A., Riener, R., Elmer, S., et al.: Decrypting the electrophysiological individuality of the human brain: identification of individuals based on resting-state EEG activity. NeuroImage 197, 470–481 (2019)
Li, D.D., Xie, L., Chai, B., Wang, Z., Yang, H.: Spatial-frequency convolutional self-attention network for EEG emotion recognition. Appl. Soft Comput. 122, 108740 (2022)
Jana, G.C., Sabath, A., Agrawal, A.: Capsule neural networks on spatio-temporal EEG frames for cross-subjectemotion recognition. Biomed. Signal Process. Control 72, 103361 (2022)
Ye, Z., Sun, T., Shi, S., et al.: Local-global active learning based on a graph convolutional network for semi-supervised classification of hyperspectral imagery. IEEE Geosci. Remote Sens. Lett. 20, 1−5 (2023)
Zhou, C.H, Zou, L.Y.: Semi-supervised Gaussian processes active learning model for imbalanced small data based on tri-training with data enhancement. IEEE Access 11, 17510–1724 (2023)
Teng, X.B., Tian, X., Doelling, K., Poeppel, D.: Theta band oscillations reflect more than entrainment: behavioral and neural evidence demonstrates an active chunking process. Eur. J. Neurosci. 48(8), 2770–2782 (2018)
Lechat, B., Hansen, K.L., Melaku, Y.A., et al.: A novel electroencephalogram-derived measure of disrupted delta wave activity during sleep predicts all-cause mortality risk. Ann. Am. Thorac. Soc. 19(4), 649–658 (2022)
Musaeus, C.S., Engedal, K., Høgh, P., et al.: EEG theta power is an early marker of cognitive decline in dementia due to Alzheimer’s disease. J. Alzheimers Dis. 64(4), 1359–1371 (2018)
Huang, Y.X., Dai, W.Z., Cai, L.W., et al.: Fast abductive learning by similarity-based consistency optimization. Adv. Neural. Inf. Process. Syst. 34, 26574–26584 (2021)
Dai, W.Z., Xu, Q., Yu, Y., et al.: Bridging machine learning and logical reasoning by abductive learning. Adv. Neural Inf. Process. Syst. 32 (2019)
Zhou, Z.-H.: Abductive learning: towards bridging machine learning and logical reasoning. Sci. China Inf. Sci. 62(7), 1–3 (2019). https://doi.org/10.1007/s11432-018-9801-4
Shi, X., Qin, P., Zhu, J., et al.: Feature extraction and classification of lower limb motion based on sEMG signal. IEEE access 8, 132882–132892 (2020)
Shi, E.Z., Yu, S.G., Kang, Y.Q., et al.: MEET: Multi-band EEG Transformer. arXiv preprint (2023)
Song, Y., Jia, X., Yang, L., et al.: Transformer-based spatial-temporal feature learning for EEG decoding. arXiv preprint arXiv:2106.11170 (2021)
Devlin, J., Chang, M.W., Lee, K., et al.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhong, T. et al. (2023). A Small-Sample Method with EEG Signals Based on Abductive Learning for Motor Imagery Decoding. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14220. Springer, Cham. https://doi.org/10.1007/978-3-031-43907-0_40
Download citation
DOI: https://doi.org/10.1007/978-3-031-43907-0_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43906-3
Online ISBN: 978-3-031-43907-0
eBook Packages: Computer ScienceComputer Science (R0)
