Topological Inference for Seizure Lateralization

Jian Yin; Duc Anh Doan; Sofia Kollia; Andrew I. Yang; Pavan Turaga; Yuan Wang

doi:10.1007/978-981-96-3294-7_18

Topological Inference for Seizure Lateralization

Jian Yin (Co-first Author), Duc Anh Doan (Co-first Author), Sofia Kollia, Andrew I. Yang, Pavan Turaga, Yuan Wang^*

^*Corresponding author for this work

Department of Biostatistics

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Topological inference based on heat kernel estimation, persistent homology, and permutation testing has shown promise in tackling various modeling challenges associated with electroencephalography (EEG) from individuals with brain network disorders. In this paper, we propose a new heat kernel estimation/smoothing method of EEG signals through Chebyshev polynomials and a fast topological permutation test to compare persistent features of two groups of smoothed signals extracted through persistent homology. We also investigate the potential of the topological inference framework in a seizure lateralization problem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Original language	English
Title of host publication	Brain Informatics
Subtitle of host publication	17th International Conference, BI 2024, Bangkok, Thailand, December 13–15, 2024, Proceedings, Part I
Editors	Sirawaj Itthipuripat, Giorgio A. Ascoli, Anan Li, Narun Pat, Hongzhi Kuai
Place of Publication	Singapore
Publisher	Springer
Pages	228-240
ISBN (Electronic)	978-981-96-3294-7
ISBN (Print)	9789819632930
DOIs	https://doi.org/10.1007/978-981-96-3294-7_18
Publication status	Published - 2025
Event	17th International Conference on Brain Informatics (BI 2024) - King Mongkut's University of Technology Thonburi, Bangkok, Thailand Duration: 13 Dec 2024 → 15 Dec 2024

Publication series

Name	Lecture Notes in Computer Science
Volume	15541
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Brain Informatics (BI 2024)
Country/Territory	Thailand
City	Bangkok
Period	13/12/24 → 15/12/24

Bibliographical note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Research Keywords

Heat Kernel
Permutation Test
Persistent Homology

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10.1007/978-981-96-3294-7_18

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Yin, J., Doan, D. A., Kollia, S., Yang, A. I., Turaga, P., & Wang, Y. (2025). Topological Inference for Seizure Lateralization. In S. Itthipuripat, G. A. Ascoli, A. Li, N. Pat, & H. Kuai (Eds.), Brain Informatics: 17th International Conference, BI 2024, Bangkok, Thailand, December 13–15, 2024, Proceedings, Part I (pp. 228-240). (Lecture Notes in Computer Science; Vol. 15541). Springer . https://doi.org/10.1007/978-981-96-3294-7_18

Yin, Jian ; Doan, Duc Anh ; Kollia, Sofia et al. / Topological Inference for Seizure Lateralization. Brain Informatics: 17th International Conference, BI 2024, Bangkok, Thailand, December 13–15, 2024, Proceedings, Part I. editor / Sirawaj Itthipuripat ; Giorgio A. Ascoli ; Anan Li ; Narun Pat ; Hongzhi Kuai. Singapore : Springer , 2025. pp. 228-240 (Lecture Notes in Computer Science).

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Yin, J, Doan, DA, Kollia, S, Yang, AI, Turaga, P & Wang, Y 2025, Topological Inference for Seizure Lateralization. in S Itthipuripat, GA Ascoli, A Li, N Pat & H Kuai (eds), Brain Informatics: 17th International Conference, BI 2024, Bangkok, Thailand, December 13–15, 2024, Proceedings, Part I. Lecture Notes in Computer Science, vol. 15541, Springer , Singapore, pp. 228-240, 17th International Conference on Brain Informatics (BI 2024) , Bangkok, Thailand, 13/12/24. https://doi.org/10.1007/978-981-96-3294-7_18

Topological Inference for Seizure Lateralization. / Yin, Jian (Co-first Author); Doan, Duc Anh (Co-first Author); Kollia, Sofia et al.
Brain Informatics: 17th International Conference, BI 2024, Bangkok, Thailand, December 13–15, 2024, Proceedings, Part I. ed. / Sirawaj Itthipuripat; Giorgio A. Ascoli; Anan Li; Narun Pat; Hongzhi Kuai. Singapore: Springer , 2025. p. 228-240 (Lecture Notes in Computer Science; Vol. 15541).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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T1 - Topological Inference for Seizure Lateralization

AU - Yin, Jian

AU - Doan, Duc Anh

AU - Kollia, Sofia

AU - Yang, Andrew I.

AU - Turaga, Pavan

AU - Wang, Yuan

N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

PY - 2025

Y1 - 2025

N2 - Topological inference based on heat kernel estimation, persistent homology, and permutation testing has shown promise in tackling various modeling challenges associated with electroencephalography (EEG) from individuals with brain network disorders. In this paper, we propose a new heat kernel estimation/smoothing method of EEG signals through Chebyshev polynomials and a fast topological permutation test to compare persistent features of two groups of smoothed signals extracted through persistent homology. We also investigate the potential of the topological inference framework in a seizure lateralization problem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

AB - Topological inference based on heat kernel estimation, persistent homology, and permutation testing has shown promise in tackling various modeling challenges associated with electroencephalography (EEG) from individuals with brain network disorders. In this paper, we propose a new heat kernel estimation/smoothing method of EEG signals through Chebyshev polynomials and a fast topological permutation test to compare persistent features of two groups of smoothed signals extracted through persistent homology. We also investigate the potential of the topological inference framework in a seizure lateralization problem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

KW - Heat Kernel

KW - Permutation Test

KW - Persistent Homology

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SN - 9789819632930

T3 - Lecture Notes in Computer Science

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EP - 240

BT - Brain Informatics

A2 - Itthipuripat, Sirawaj

A2 - Ascoli, Giorgio A.

A2 - Li, Anan

A2 - Pat, Narun

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PB - Springer

CY - Singapore

T2 - 17th International Conference on Brain Informatics (BI 2024)

Y2 - 13 December 2024 through 15 December 2024

ER -

Yin J, Doan DA, Kollia S, Yang AI, Turaga P, Wang Y. Topological Inference for Seizure Lateralization. In Itthipuripat S, Ascoli GA, Li A, Pat N, Kuai H, editors, Brain Informatics: 17th International Conference, BI 2024, Bangkok, Thailand, December 13–15, 2024, Proceedings, Part I. Singapore: Springer . 2025. p. 228-240. (Lecture Notes in Computer Science). Epub 2025 Apr 2. doi: 10.1007/978-981-96-3294-7_18

Topological Inference for Seizure Lateralization

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