ICA on sensor or source data: A comparison study in deriving resting state networks from EEG

Chuang Li; Han Yuan; Diamond Urbano; Yoon Hee Cha; Lei Ding

doi:10.1109/EMBC.2017.8037637

ICA on sensor or source data: A comparison study in deriving resting state networks from EEG

Chuang Li, Han Yuan, Diamond Urbano, Yoon Hee Cha, Lei Ding

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

2 Scopus citations

Abstract

Resting state networks (RSNs) are human brain networks formed by spontaneous activity fluctuations in distributed brain regions when people are in task-free and awake state. RSNs have been so far extensively studied using functional magnetic resonance imaging (fMRI). Recently, electroencephalography (EEG) and magnetoencephalography (MEG) have also been used to derive RSNs, in which independent component analysis (ICA) is the key step. In these studies, ICA has been either directly applied to recorded data at sensors (sensor-space ICA) or estimated source data from sensors using inverse source imaging techniques (source-space ICA). Both sensor-space and source-space ICAs have demonstrated the capability in finding RSNs from EEG/MEG data and their results showed strong correlations to fMRI RSNs. However, their performance was hardly compared even differences have been observed in their results. In the present study, we compared the source-space and sensor-space ICAs in reconstructing spatial, temporal and spectral features of RSNs in both simulated and real EEG data. Results from simulated data indicated that the source-space ICA has better performance in reconstructing spatial, temporal, and spectral feature of RSNs. Results from resting-sate EEG data in seven healthy participants also showed the difference between two procedures and, through the comparison with RSN templates constructed from fMRI data, the source-space ICA indicated relatively better performance than the sensor-space ICA.

Original language	English (US)
Title of host publication	2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publication	Smarter Technology for a Healthier World, EMBC 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3604-3607
Number of pages	4
ISBN (Electronic)	9781509028092
DOIs	https://doi.org/10.1109/EMBC.2017.8037637
State	Published - Sep 13 2017
Externally published	Yes
Event	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of Duration: Jul 11 2017 → Jul 15 2017

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Other

Other	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Country/Territory	Korea, Republic of
City	Jeju Island
Period	7/11/17 → 7/15/17

Bibliographical note

Funding Information:
This work was supported in part by NSF CAREER ECCS-0955260, NSF RII Track-2 FEC 1539068, NIH/NIDCD R03 DC010451, and an equipment grant from the MdDS Balance Disorders Foundation. Asterisk indicates corresponding author.

Publisher Copyright:
© 2017 IEEE.

Publisher link

10.1109/EMBC.2017.8037637

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Li, C., Yuan, H., Urbano, D., Cha, Y. H., & Ding, L. (2017). ICA on sensor or source data: A comparison study in deriving resting state networks from EEG. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings (pp. 3604-3607). Article 8037637 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2017.8037637

ICA on sensor or source data: A comparison study in deriving resting state networks from EEG. / Li, Chuang; Yuan, Han; Urbano, Diamond et al.
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 3604-3607 8037637 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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

Li, C, Yuan, H, Urbano, D, Cha, YH & Ding, L 2017, ICA on sensor or source data: A comparison study in deriving resting state networks from EEG. in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings., 8037637, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., pp. 3604-3607, 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017, Jeju Island, Korea, Republic of, 7/11/17. https://doi.org/10.1109/EMBC.2017.8037637

Li C, Yuan H, Urbano D, Cha YH, Ding L. ICA on sensor or source data: A comparison study in deriving resting state networks from EEG. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 3604-3607. 8037637. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2017.8037637

Li, Chuang ; Yuan, Han ; Urbano, Diamond et al. / ICA on sensor or source data : A comparison study in deriving resting state networks from EEG. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 3604-3607 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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abstract = "Resting state networks (RSNs) are human brain networks formed by spontaneous activity fluctuations in distributed brain regions when people are in task-free and awake state. RSNs have been so far extensively studied using functional magnetic resonance imaging (fMRI). Recently, electroencephalography (EEG) and magnetoencephalography (MEG) have also been used to derive RSNs, in which independent component analysis (ICA) is the key step. In these studies, ICA has been either directly applied to recorded data at sensors (sensor-space ICA) or estimated source data from sensors using inverse source imaging techniques (source-space ICA). Both sensor-space and source-space ICAs have demonstrated the capability in finding RSNs from EEG/MEG data and their results showed strong correlations to fMRI RSNs. However, their performance was hardly compared even differences have been observed in their results. In the present study, we compared the source-space and sensor-space ICAs in reconstructing spatial, temporal and spectral features of RSNs in both simulated and real EEG data. Results from simulated data indicated that the source-space ICA has better performance in reconstructing spatial, temporal, and spectral feature of RSNs. Results from resting-sate EEG data in seven healthy participants also showed the difference between two procedures and, through the comparison with RSN templates constructed from fMRI data, the source-space ICA indicated relatively better performance than the sensor-space ICA.",

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ICA on sensor or source data: A comparison study in deriving resting state networks from EEG

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