American Institute of Mathematical Sciences

Advanced
2006, 3(4): 697-716. doi: 10.3934/mbe.2006.3.697

Complex spatio-temporal features in meg data

Francesca Sapuppo 1, , Elena Umana 2, , Mattia Frasca 3, , Manuela La Rosa 4, , David Shannahoff-Khalsa 5, , Luigi Fortuna 3, and  Maide Bucolo 1,

1. 

Dipartimento di Ingegneria Elettrica, Elettronica e dei Sistemi, Universita degli Studi di Catania, Viale A. Doria 6, 95125 Catania, Italy, Italy
2. 

Dipartimento di Ingegneria Elettrica, Elettronica e dei Sistemi, Universitá degli Studi di Catania, V.le A, Doria 6, 95125 Catania, Italy
3. 

Dipartimento di Ingegneria Elettrica Elettronica e dei Sistemi, Facoltà di Ingegneria, Università degli Studi di Catania, viale A. Doria 6, 95125 Catania, Italy, Italy
4. 

PST Group, Corporate R&D, STMicroelectronics, Catania site, Stradale Primosole 50, 95121 Catania
5. 

Institute for Nonlinear Science, University of California, San Diego, 9500 Gilman Dr., La Jolla, 92093-0402 CA, United States

Received  February 2006 Revised  June 2006 Published  August 2006

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.
Keywords: complexity, d-infinite (d∞)., Magnetoencephalography.
Mathematics Subject Classification: 92D3.
Citation: Francesca Sapuppo, Elena Umana, Mattia Frasca, Manuela La Rosa, David Shannahoff-Khalsa, Luigi Fortuna, Maide Bucolo. Complex spatio-temporal features in meg data. Mathematical Biosciences & Engineering, 2006, 3 (4) : 697-716. doi: 10.3934/mbe.2006.3.697
[1]

Mustapha Yebdri. Existence of $ \mathcal{D}- $pullback attractor for an infinite dimensional dynamical system. Discrete & Continuous Dynamical Systems - B, 2021  doi: 10.3934/dcdsb.2021036
[2]

Wenjing Liu, Rong Yang, Xin-Guang Yang. Dynamics of a 3D Brinkman-Forchheimer equation with infinite delay. Communications on Pure & Applied Analysis, 2021, 20 (5) : 1907-1930. doi: 10.3934/cpaa.2021052
[3]

Roland Zweimüller. Asymptotic orbit complexity of infinite measure preserving transformations. Discrete & Continuous Dynamical Systems, 2006, 15 (1) : 353-366. doi: 10.3934/dcds.2006.15.353
[4]

Marta Lewicka, Mohammadreza Raoofi. A stability result for the Stokes-Boussinesq equations in infinite 3d channels. Communications on Pure & Applied Analysis, 2013, 12 (6) : 2615-2625. doi: 10.3934/cpaa.2013.12.2615
[5]

Julia García-Luengo, Pedro Marín-Rubio, José Real. Regularity of pullback attractors and attraction in $H^1$ in arbitrarily large finite intervals for 2D Navier-Stokes equations with infinite delay. Discrete & Continuous Dynamical Systems, 2014, 34 (1) : 181-201. doi: 10.3934/dcds.2014.34.181
[6]

J. Becker, M. Ferreira, B.M.P.M. Oliveira, A.A. Pinto. R&d dynamics. Conference Publications, 2013, 2013 (special) : 61-68. doi: 10.3934/proc.2013.2013.61
[7]

María Isabel Cortez. $Z^d$ Toeplitz arrays. Discrete & Continuous Dynamical Systems, 2006, 15 (3) : 859-881. doi: 10.3934/dcds.2006.15.859
[8]

Carolin Kreisbeck. A note on $3$d-$1$d dimension reduction with differential constraints. Discrete & Continuous Dynamical Systems - S, 2017, 10 (1) : 55-73. doi: 10.3934/dcdss.2017003
[9]

Sebastián Donoso. Enveloping semigroups of systems of order d. Discrete & Continuous Dynamical Systems, 2014, 34 (7) : 2729-2740. doi: 10.3934/dcds.2014.34.2729
[10]

Ted Greenwood. Superstar of the Sloan Minority Ph.D. Program. Mathematical Biosciences & Engineering, 2013, 10 (5&6) : 1539-1540. doi: 10.3934/mbe.2013.10.1539
[11]

Mark Pollicott. $\mathbb Z^d$-covers of horosphere foliations. Discrete & Continuous Dynamical Systems, 2000, 6 (1) : 147-154. doi: 10.3934/dcds.2000.6.147
[12]

Yong Zhou. Remarks on regularities for the 3D MHD equations. Discrete & Continuous Dynamical Systems, 2005, 12 (5) : 881-886. doi: 10.3934/dcds.2005.12.881
[13]

Tian Ma, Shouhong Wang. Global structure of 2-D incompressible flows. Discrete & Continuous Dynamical Systems, 2001, 7 (2) : 431-445. doi: 10.3934/dcds.2001.7.431
[14]

Hyeong-Ohk Bae, Bum Ja Jin. Estimates of the wake for the 3D Oseen equations. Discrete & Continuous Dynamical Systems - B, 2008, 10 (1) : 1-18. doi: 10.3934/dcdsb.2008.10.1
[15]

Gianluca Crippa, Elizaveta Semenova, Stefano Spirito. Strong continuity for the 2D Euler equations. Kinetic & Related Models, 2015, 8 (4) : 685-689. doi: 10.3934/krm.2015.8.685
[16]

Yu. Dabaghian, R. V. Jensen, R. Blümel. Integrability in 1D quantum chaos. Conference Publications, 2003, 2003 (Special) : 206-212. doi: 10.3934/proc.2003.2003.206
[17]

Bernd Kawohl, Guido Sweers. On a formula for sets of constant width in 2d. Communications on Pure & Applied Analysis, 2019, 18 (4) : 2117-2131. doi: 10.3934/cpaa.2019095
[18]

Robert P. Gilbert, Philippe Guyenne, Ying Liu. Modeling of the kinetics of vitamin D$_3$ in osteoblastic cells. Mathematical Biosciences & Engineering, 2013, 10 (2) : 319-344. doi: 10.3934/mbe.2013.10.319
[19]

Ka Kit Tung, Wendell Welch Orlando. On the differences between 2D and QG turbulence. Discrete & Continuous Dynamical Systems - B, 2003, 3 (2) : 145-162. doi: 10.3934/dcdsb.2003.3.145
[20]

Julien Cividini. Pattern formation in 2D traffic flows. Discrete & Continuous Dynamical Systems - S, 2014, 7 (3) : 395-409. doi: 10.3934/dcdss.2014.7.395

2018 Impact Factor: 1.313

Tools

Metrics

  • PDF downloads (28)
  • HTML views (0)
  • Cited by (1)

Other articles
by authors

[Back to Top]

Copyright © 2021 American Institute of Mathematical Sciences