Combined high resolution EEG and MEG data for linear inverse estimate of human event-related cortical activity

F. Babiloni; C. Del Gratta; F. Carducci; C. Babiloni; G. M. Roberti; V. Pizzella; P. M. Rossini; G. L. Romani; A. Urbano

Combined high resolution EEG and MEG data for linear inverse estimate of human event-related cortical activity

F. Babiloni, C. Del Gratta, F. Carducci, C. Babiloni, G. M. Roberti, V. Pizzella, P. M. Rossini, G. L. Romani, A. Urbano

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

Abstract

A new spatial deblurring method for the modeling of human event-related cortical activity from electroencephalography (EEG) and magnetoencephalography (MEG) data is proposed. This method includes high surface sampling of EEG-MEG data (128-50 sensors), realistic magnetic resonance-constructed subject's multi-compartment (scalp, skull, dura mater, cortex) head model, multi-dipole source model, and regularized linear inverse estimate based on boundary element mathematics. As a novelty, linear inverse estimates are regularized not assuming that covariance of background electromagnetic noise between sensors was zero. EEG and MEG data were recorded (separate sessions) while two normal subjects executed voluntary right one-digit movements. Linear inverse estimates of movement-related cortical activity from the combined EEG and MEG data showed higher spatial information content than those obtained from the MEG and EEG data considered separately. In conclusion, the new spatial deblurring method represents a powerful multi-modal neuroimaging approach to the non invasive study of human brain functions.

Original language	English
Title of host publication	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Editors	H.K. Chang, Y.T. Zhang
Publisher	IEEE
Pages	2151-2154
Number of pages	4
Volume	4
Publication status	Published - 1998
Event	Proceedings of the 1998 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 4 (of 6) - Hong Kong, China Duration: Oct 29 1998 → Nov 1 1998

Other

Other	Proceedings of the 1998 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 4 (of 6)
City	Hong Kong, China
Period	10/29/98 → 11/1/98

ASJC Scopus subject areas

Bioengineering

Cite this

Babiloni, F., Del Gratta, C., Carducci, F., Babiloni, C., Roberti, G. M., Pizzella, V., Rossini, P. M., Romani, G. L., & Urbano, A. (1998). Combined high resolution EEG and MEG data for linear inverse estimate of human event-related cortical activity. In H. K. Chang, & Y. T. Zhang (Eds.), Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings (Vol. 4, pp. 2151-2154). IEEE.

Combined high resolution EEG and MEG data for linear inverse estimate of human event-related cortical activity. / Babiloni, F.; Del Gratta, C.; Carducci, F. et al.

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. ed. / H.K. Chang; Y.T. Zhang. Vol. 4 IEEE, 1998. p. 2151-2154.

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

Babiloni, F, Del Gratta, C, Carducci, F, Babiloni, C, Roberti, GM, Pizzella, V, Rossini, PM, Romani, GL & Urbano, A 1998, Combined high resolution EEG and MEG data for linear inverse estimate of human event-related cortical activity. in HK Chang & YT Zhang (eds), Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. vol. 4, IEEE, pp. 2151-2154, Proceedings of the 1998 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 4 (of 6), Hong Kong, China, 10/29/98.

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title = "Combined high resolution EEG and MEG data for linear inverse estimate of human event-related cortical activity",

abstract = "A new spatial deblurring method for the modeling of human event-related cortical activity from electroencephalography (EEG) and magnetoencephalography (MEG) data is proposed. This method includes high surface sampling of EEG-MEG data (128-50 sensors), realistic magnetic resonance-constructed subject's multi-compartment (scalp, skull, dura mater, cortex) head model, multi-dipole source model, and regularized linear inverse estimate based on boundary element mathematics. As a novelty, linear inverse estimates are regularized not assuming that covariance of background electromagnetic noise between sensors was zero. EEG and MEG data were recorded (separate sessions) while two normal subjects executed voluntary right one-digit movements. Linear inverse estimates of movement-related cortical activity from the combined EEG and MEG data showed higher spatial information content than those obtained from the MEG and EEG data considered separately. In conclusion, the new spatial deblurring method represents a powerful multi-modal neuroimaging approach to the non invasive study of human brain functions.",

author = "F. Babiloni and {Del Gratta}, C. and F. Carducci and C. Babiloni and Roberti, {G. M.} and V. Pizzella and Rossini, {P. M.} and Romani, {G. L.} and A. Urbano",

year = "1998",

language = "English",

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AU - Babiloni, F.

AU - Del Gratta, C.

AU - Carducci, F.

AU - Babiloni, C.

AU - Roberti, G. M.

AU - Pizzella, V.

AU - Rossini, P. M.

AU - Romani, G. L.

AU - Urbano, A.

PY - 1998

Y1 - 1998

N2 - A new spatial deblurring method for the modeling of human event-related cortical activity from electroencephalography (EEG) and magnetoencephalography (MEG) data is proposed. This method includes high surface sampling of EEG-MEG data (128-50 sensors), realistic magnetic resonance-constructed subject's multi-compartment (scalp, skull, dura mater, cortex) head model, multi-dipole source model, and regularized linear inverse estimate based on boundary element mathematics. As a novelty, linear inverse estimates are regularized not assuming that covariance of background electromagnetic noise between sensors was zero. EEG and MEG data were recorded (separate sessions) while two normal subjects executed voluntary right one-digit movements. Linear inverse estimates of movement-related cortical activity from the combined EEG and MEG data showed higher spatial information content than those obtained from the MEG and EEG data considered separately. In conclusion, the new spatial deblurring method represents a powerful multi-modal neuroimaging approach to the non invasive study of human brain functions.

AB - A new spatial deblurring method for the modeling of human event-related cortical activity from electroencephalography (EEG) and magnetoencephalography (MEG) data is proposed. This method includes high surface sampling of EEG-MEG data (128-50 sensors), realistic magnetic resonance-constructed subject's multi-compartment (scalp, skull, dura mater, cortex) head model, multi-dipole source model, and regularized linear inverse estimate based on boundary element mathematics. As a novelty, linear inverse estimates are regularized not assuming that covariance of background electromagnetic noise between sensors was zero. EEG and MEG data were recorded (separate sessions) while two normal subjects executed voluntary right one-digit movements. Linear inverse estimates of movement-related cortical activity from the combined EEG and MEG data showed higher spatial information content than those obtained from the MEG and EEG data considered separately. In conclusion, the new spatial deblurring method represents a powerful multi-modal neuroimaging approach to the non invasive study of human brain functions.

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ER -

Combined high resolution EEG and MEG data for linear inverse estimate of human event-related cortical activity

Abstract

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ASJC Scopus subject areas

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