Spatio-temporal analysis of P300 using ICA and SSLOFO

Bo Hong; Hesheng Liu; Paul H. Schimpf; Shangkai Gao; Nitish V. Thakor

doi:10.1109/CNE.2005.1419706

Spatio-temporal analysis of P300 using ICA and SSLOFO

Bo Hong, Hesheng Liu, Paul H. Schimpf, Shangkai Gao, Nitish V. Thakor

School of Medicine

Research output: Contribution to conference › Paper › peer-review

Abstract

Spatial information of EEG source activity revealed by inverse methods may contribute to an improvement of the BCI systems. This paper proposes an approach that integrates the Independent Component Analysis (ICA) and a newly developed inverse algorithm termed SSLOFO to robustly reconstruct cortical sources of P300. The target independent components are first extracted using a spatio-temporal optimization process and then SSLOFO is employed to localize the sources of the target components. Preliminary studies demonstrate our method is able to localize sources of P300 based on 5-trial-averaged EEG and the results are consistent with the findings of other functional imaging studies such as fMRI. The robustness of our approach is also proved by a study which indicates the P300 sources are stably reconstructed around the left and right TPJ areas.

Original language	English (US)
Pages	640-643
Number of pages	4
DOIs	https://doi.org/10.1109/CNE.2005.1419706
State	Published - 2005
Event	2nd International IEEE EMBS Conference on Neural Engineering, 2005 - Arlington, VA, United States Duration: Mar 16 2005 → Mar 19 2005

Other

Other	2nd International IEEE EMBS Conference on Neural Engineering, 2005
Country/Territory	United States
City	Arlington, VA
Period	3/16/05 → 3/19/05

Keywords

BCI
ICA
Inverse problem
P300
SSLOFO

ASJC Scopus subject areas

Engineering(all)

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10.1109/CNE.2005.1419706

Cite this

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title = "Spatio-temporal analysis of P300 using ICA and SSLOFO",

abstract = "Spatial information of EEG source activity revealed by inverse methods may contribute to an improvement of the BCI systems. This paper proposes an approach that integrates the Independent Component Analysis (ICA) and a newly developed inverse algorithm termed SSLOFO to robustly reconstruct cortical sources of P300. The target independent components are first extracted using a spatio-temporal optimization process and then SSLOFO is employed to localize the sources of the target components. Preliminary studies demonstrate our method is able to localize sources of P300 based on 5-trial-averaged EEG and the results are consistent with the findings of other functional imaging studies such as fMRI. The robustness of our approach is also proved by a study which indicates the P300 sources are stably reconstructed around the left and right TPJ areas.",

keywords = "BCI, ICA, Inverse problem, P300, SSLOFO",

author = "Bo Hong and Hesheng Liu and Schimpf, {Paul H.} and Shangkai Gao and Thakor, {Nitish V.}",

year = "2005",

doi = "10.1109/CNE.2005.1419706",

language = "English (US)",

pages = "640--643",

note = "2nd International IEEE EMBS Conference on Neural Engineering, 2005 ; Conference date: 16-03-2005 Through 19-03-2005",

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T1 - Spatio-temporal analysis of P300 using ICA and SSLOFO

AU - Hong, Bo

AU - Liu, Hesheng

AU - Schimpf, Paul H.

AU - Gao, Shangkai

AU - Thakor, Nitish V.

PY - 2005

Y1 - 2005

N2 - Spatial information of EEG source activity revealed by inverse methods may contribute to an improvement of the BCI systems. This paper proposes an approach that integrates the Independent Component Analysis (ICA) and a newly developed inverse algorithm termed SSLOFO to robustly reconstruct cortical sources of P300. The target independent components are first extracted using a spatio-temporal optimization process and then SSLOFO is employed to localize the sources of the target components. Preliminary studies demonstrate our method is able to localize sources of P300 based on 5-trial-averaged EEG and the results are consistent with the findings of other functional imaging studies such as fMRI. The robustness of our approach is also proved by a study which indicates the P300 sources are stably reconstructed around the left and right TPJ areas.

AB - Spatial information of EEG source activity revealed by inverse methods may contribute to an improvement of the BCI systems. This paper proposes an approach that integrates the Independent Component Analysis (ICA) and a newly developed inverse algorithm termed SSLOFO to robustly reconstruct cortical sources of P300. The target independent components are first extracted using a spatio-temporal optimization process and then SSLOFO is employed to localize the sources of the target components. Preliminary studies demonstrate our method is able to localize sources of P300 based on 5-trial-averaged EEG and the results are consistent with the findings of other functional imaging studies such as fMRI. The robustness of our approach is also proved by a study which indicates the P300 sources are stably reconstructed around the left and right TPJ areas.

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KW - ICA

KW - Inverse problem

KW - P300

KW - SSLOFO

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T2 - 2nd International IEEE EMBS Conference on Neural Engineering, 2005

Y2 - 16 March 2005 through 19 March 2005

ER -

Spatio-temporal analysis of P300 using ICA and SSLOFO

Abstract

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Keywords

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