A Small-Sample Method with EEG Signals Based on Abductive Learning for Motor Imagery Decoding

Tianyang Zhong; Xiaozheng Wei; Enze Shi; Jiaxing Gao; Chong Ma; Yaonai Wei; Songyao Zhang; Lei Guo; Junwei Han; Tianming Liu; Tuo Zhang

doi:10.1007/978-3-031-43907-0_40

A Small-Sample Method with EEG Signals Based on Abductive Learning for Motor Imagery Decoding

Tianyang Zhong, Xiaozheng Wei, Enze Shi, Jiaxing Gao, Chong Ma, Yaonai Wei, Songyao Zhang, Lei Guo, Junwei Han, Tianming Liu, Tuo Zhang

School of Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings
Editors	Hayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	416-424
Number of pages	9
ISBN (Print)	9783031439063
DOIs	https://doi.org/10.1007/978-3-031-43907-0_40
State	Published - 2023
Event	26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 - Vancouver, Canada Duration: 8 Oct 2023 → 12 Oct 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14220 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
Country/Territory	Canada
City	Vancouver
Period	8/10/23 → 12/10/23

Keywords

Abductive Learning
MI Decoding
Small-Sample
Visualization

Access to Document

10.1007/978-3-031-43907-0_40

Cite this

Zhong, T., Wei, X., Shi, E., Gao, J., Ma, C., Wei, Y., Zhang, S., Guo, L., Han, J., Liu, T., & Zhang, T. (2023). A Small-Sample Method with EEG Signals Based on Abductive Learning for Motor Imagery Decoding. In H. Greenspan, H. Greenspan, A. Madabhushi, P. Mousavi, S. Salcudean, J. Duncan, T. Syeda-Mahmood, & R. Taylor (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings (pp. 416-424). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14220 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-43907-0_40

Zhong, Tianyang ; Wei, Xiaozheng ; Shi, Enze et al. / A Small-Sample Method with EEG Signals Based on Abductive Learning for Motor Imagery Decoding. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. editor / Hayit Greenspan ; Hayit Greenspan ; Anant Madabhushi ; Parvin Mousavi ; Septimiu Salcudean ; James Duncan ; Tanveer Syeda-Mahmood ; Russell Taylor. Springer Science and Business Media Deutschland GmbH, 2023. pp. 416-424 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{459c4ef5960c4a91ba5454893a85b710,

title = "A Small-Sample Method with EEG Signals Based on Abductive Learning for Motor Imagery Decoding",

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.",

keywords = "Abductive Learning, MI Decoding, Small-Sample, Visualization",

author = "Tianyang Zhong and Xiaozheng Wei and Enze Shi and Jiaxing Gao and Chong Ma and Yaonai Wei and Songyao Zhang and Lei Guo and Junwei Han and Tianming Liu and Tuo Zhang",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 ; Conference date: 08-10-2023 Through 12-10-2023",

year = "2023",

doi = "10.1007/978-3-031-43907-0_40",

language = "英语",

isbn = "9783031439063",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "416--424",

editor = "Hayit Greenspan and Hayit Greenspan and Anant Madabhushi and Parvin Mousavi and Septimiu Salcudean and James Duncan and Tanveer Syeda-Mahmood and Russell Taylor",

booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings",

}

Zhong, T, Wei, X, Shi, E, Gao, J, Ma, C, Wei, Y, Zhang, S, Guo, L , Han, J, Liu, T & Zhang, T 2023, A Small-Sample Method with EEG Signals Based on Abductive Learning for Motor Imagery Decoding. in H Greenspan, H Greenspan, A Madabhushi, P Mousavi, S Salcudean, J Duncan, T Syeda-Mahmood & R Taylor (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14220 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 416-424, 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, Vancouver, Canada, 8/10/23. https://doi.org/10.1007/978-3-031-43907-0_40

A Small-Sample Method with EEG Signals Based on Abductive Learning for Motor Imagery Decoding. / Zhong, Tianyang; Wei, Xiaozheng; Shi, Enze et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. ed. / Hayit Greenspan; Hayit Greenspan; Anant Madabhushi; Parvin Mousavi; Septimiu Salcudean; James Duncan; Tanveer Syeda-Mahmood; Russell Taylor. Springer Science and Business Media Deutschland GmbH, 2023. p. 416-424 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14220 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Small-Sample Method with EEG Signals Based on Abductive Learning for Motor Imagery Decoding

AU - Zhong, Tianyang

AU - Wei, Xiaozheng

AU - Shi, Enze

AU - Gao, Jiaxing

AU - Ma, Chong

AU - Wei, Yaonai

AU - Zhang, Songyao

AU - Guo, Lei

AU - Han, Junwei

AU - Liu, Tianming

AU - Zhang, Tuo

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Abductive Learning

KW - MI Decoding

KW - Small-Sample

KW - Visualization

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DO - 10.1007/978-3-031-43907-0_40

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SN - 9783031439063

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings

A2 - Greenspan, Hayit

A2 - Madabhushi, Anant

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A2 - Salcudean, Septimiu

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PB - Springer Science and Business Media Deutschland GmbH

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Zhong T, Wei X, Shi E, Gao J, Ma C, Wei Y et al. A Small-Sample Method with EEG Signals Based on Abductive Learning for Motor Imagery Decoding. In Greenspan H, Greenspan H, Madabhushi A, Mousavi P, Salcudean S, Duncan J, Syeda-Mahmood T, Taylor R, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 416-424. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-43907-0_40

A Small-Sample Method with EEG Signals Based on Abductive Learning for Motor Imagery Decoding

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Keywords

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