Complex spatio-temporal features in meg data
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1.
Dipartimento di Ingegneria Elettrica, Elettronica e dei Sistemi, Universita degli Studi di Catania, Viale A. Doria 6, 95125 Catania
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2.
Dipartimento di Ingegneria Elettrica, Elettronica e dei Sistemi, Universitá degli Studi di Catania, V.le A, Doria 6, 95125 Catania
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3.
Dipartimento di Ingegneria Elettrica Elettronica e dei Sistemi, Facoltà di Ingegneria, Università degli Studi di Catania, viale A. Doria 6, 95125 Catania
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4.
PST Group, Corporate R&D, STMicroelectronics, Catania site, Stradale Primosole 50, 95121 Catania
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5.
Institute for Nonlinear Science, University of California, San Diego, 9500 Gilman Dr., La Jolla, 92093-0402 CA
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Received:
01 February 2006
Accepted:
29 June 2018
Published:
01 August 2006
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MSC :
92D30.
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Magnetoencephalography (MEG) brain signals are studied using
a method for characterizing complex nonlinear dynamics. This approach uses
the value of $d_\infty$ (d-infinite) to characterize the system’s asymptotic chaotic
behavior. A novel procedure has been developed to extract this parameter
from time series when the system’s structure and laws are unknown. The implementation
of the algorithm was proven to be general and computationally
efficient. The information characterized by this parameter is furthermore independent
and complementary to the signal power since it considers signals
normalized with respect to their amplitude. The algorithm implemented here
is applied to whole-head 148 channel MEG data during two highly structured
yogic breathing meditation techniques. Results are presented for the spatiotemporal
distributions of the calculated $d_\infty$ on the MEG channels, and they
are compared for the different phases of the yogic protocol. The algorithm was
applied to six MEG data sets recorded over a three-month period. This provides
the opportunity of verifying the consistency of unique spatio-temporal
features found in specific protocol phases and the chance to investigate the
potential long term effects of these yogic techniques. Differences among the
spatio-temporal patterns related to each phase were found, and they were
independent of the power spatio-temporal distributions that are based on conventional
analysis. This approach also provides an opportunity to compare
both methods and possibly gain complementary information.
Citation: Francesca Sapuppo, Elena Umana, Mattia Frasca, Manuela La Rosa, David Shannahoff-Khalsa, Luigi Fortuna, Maide Bucolo. Complex spatio-temporal features in meg data[J]. Mathematical Biosciences and Engineering, 2006, 3(4): 697-716. doi: 10.3934/mbe.2006.3.697
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Abstract
Magnetoencephalography (MEG) brain signals are studied using
a method for characterizing complex nonlinear dynamics. This approach uses
the value of $d_\infty$ (d-infinite) to characterize the system’s asymptotic chaotic
behavior. A novel procedure has been developed to extract this parameter
from time series when the system’s structure and laws are unknown. The implementation
of the algorithm was proven to be general and computationally
efficient. The information characterized by this parameter is furthermore independent
and complementary to the signal power since it considers signals
normalized with respect to their amplitude. The algorithm implemented here
is applied to whole-head 148 channel MEG data during two highly structured
yogic breathing meditation techniques. Results are presented for the spatiotemporal
distributions of the calculated $d_\infty$ on the MEG channels, and they
are compared for the different phases of the yogic protocol. The algorithm was
applied to six MEG data sets recorded over a three-month period. This provides
the opportunity of verifying the consistency of unique spatio-temporal
features found in specific protocol phases and the chance to investigate the
potential long term effects of these yogic techniques. Differences among the
spatio-temporal patterns related to each phase were found, and they were
independent of the power spatio-temporal distributions that are based on conventional
analysis. This approach also provides an opportunity to compare
both methods and possibly gain complementary information.
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