Localizing Current Dipoles from EEG Data Using a Birth-Death Process

Keita Nakamura; Sho Sonoda; Hideitsu Hino; Masahiro Kawasaki; Shotaro Akaho; Noboru Murata

doi:10.1109/BIBM.2018.8621504

Localizing Current Dipoles from EEG Data Using a Birth-Death Process

Keita Nakamura, Sho Sonoda, Hideitsu Hino, Masahiro Kawasaki, Shotaro Akaho, Noboru Murata

School of Advanced Science and Engineering

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

Abstract

A common approach to the electroencephalogram (EEG) source localization problem is to estimate the states of current dipoles. However, the dipole estimation problem is difficult because not only is it an inverse problem but also the number of dipoles can change over time. In this paper, we model the relationship between current dipoles and EEG observations using a state-space model where the creation and annihilation of dipoles is represented as a birth-death process. We estimate the dipoles' positions and moments with a Rao-Blackwellized particle filter and estimate whether a new dipole has been created or an existing one annihilated via the Bayesian information criterion. Experiments on both synthetic and real data show that the proposed model and estimation method can effectively estimate the number and positions of the dipoles.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Editors	Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2645-2651
Number of pages	7
ISBN (Electronic)	9781538654880
DOIs	https://doi.org/10.1109/BIBM.2018.8621504
Publication status	Published - 2019 Jan 21
Event	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: 2018 Dec 3 → 2018 Dec 6

Publication series

Name	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/Territory	Spain
City	Madrid
Period	18/12/3 → 18/12/6

Keywords

model selection
sequential Bayesian estimation
source localization
state-space model

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics

Access to Document

10.1109/BIBM.2018.8621504

Cite this

Nakamura, K., Sonoda, S., Hino, H., Kawasaki, M., Akaho, S., & Murata, N. (2019). Localizing Current Dipoles from EEG Data Using a Birth-Death Process. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 2645-2651). Article 8621504 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621504

Localizing Current Dipoles from EEG Data Using a Birth-Death Process. / Nakamura, Keita; Sonoda, Sho; Hino, Hideitsu et al.
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2645-2651 8621504 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

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

Nakamura, K, Sonoda, S, Hino, H, Kawasaki, M, Akaho, S & Murata, N 2019, Localizing Current Dipoles from EEG Data Using a Birth-Death Process. in H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018., 8621504, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 2645-2651, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 18/12/3. https://doi.org/10.1109/BIBM.2018.8621504

Nakamura K, Sonoda S, Hino H, Kawasaki M, Akaho S, Murata N. Localizing Current Dipoles from EEG Data Using a Birth-Death Process. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 2645-2651. 8621504. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). doi: 10.1109/BIBM.2018.8621504

Nakamura, Keita ; Sonoda, Sho ; Hino, Hideitsu et al. / Localizing Current Dipoles from EEG Data Using a Birth-Death Process. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 2645-2651 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

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abstract = "A common approach to the electroencephalogram (EEG) source localization problem is to estimate the states of current dipoles. However, the dipole estimation problem is difficult because not only is it an inverse problem but also the number of dipoles can change over time. In this paper, we model the relationship between current dipoles and EEG observations using a state-space model where the creation and annihilation of dipoles is represented as a birth-death process. We estimate the dipoles' positions and moments with a Rao-Blackwellized particle filter and estimate whether a new dipole has been created or an existing one annihilated via the Bayesian information criterion. Experiments on both synthetic and real data show that the proposed model and estimation method can effectively estimate the number and positions of the dipoles.",

keywords = "model selection, sequential Bayesian estimation, source localization, state-space model",

author = "Keita Nakamura and Sho Sonoda and Hideitsu Hino and Masahiro Kawasaki and Shotaro Akaho and Noboru Murata",

note = "Funding Information: Part of this work is supported by KAKENHI grant numbers 16K16108,16H02842,18K18113, and JST CREST JPMJCR1761. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 ; Conference date: 03-12-2018 Through 06-12-2018",

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AU - Sonoda, Sho

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AU - Murata, Noboru

PY - 2019/1/21

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N2 - A common approach to the electroencephalogram (EEG) source localization problem is to estimate the states of current dipoles. However, the dipole estimation problem is difficult because not only is it an inverse problem but also the number of dipoles can change over time. In this paper, we model the relationship between current dipoles and EEG observations using a state-space model where the creation and annihilation of dipoles is represented as a birth-death process. We estimate the dipoles' positions and moments with a Rao-Blackwellized particle filter and estimate whether a new dipole has been created or an existing one annihilated via the Bayesian information criterion. Experiments on both synthetic and real data show that the proposed model and estimation method can effectively estimate the number and positions of the dipoles.

AB - A common approach to the electroencephalogram (EEG) source localization problem is to estimate the states of current dipoles. However, the dipole estimation problem is difficult because not only is it an inverse problem but also the number of dipoles can change over time. In this paper, we model the relationship between current dipoles and EEG observations using a state-space model where the creation and annihilation of dipoles is represented as a birth-death process. We estimate the dipoles' positions and moments with a Rao-Blackwellized particle filter and estimate whether a new dipole has been created or an existing one annihilated via the Bayesian information criterion. Experiments on both synthetic and real data show that the proposed model and estimation method can effectively estimate the number and positions of the dipoles.

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Localizing Current Dipoles from EEG Data Using a Birth-Death Process

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Keywords

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