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Mathematical Biosciences and Engineering

2006, Volume 3, Issue 4: 697-716. doi: 10.3934/mbe.2006.3.697
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Complex spatio-temporal features in meg data

  • 1.
    Dipartimento di Ingegneria Elettrica, Elettronica e dei Sistemi, Universita degli Studi di Catania, Viale A. Doria 6, 95125 Catania
  • 2.
    Dipartimento di Ingegneria Elettrica, Elettronica e dei Sistemi, Universitá degli Studi di Catania, V.le A, Doria 6, 95125 Catania
  • 3.
    Dipartimento di Ingegneria Elettrica Elettronica e dei Sistemi, Facoltà di Ingegneria, Università degli Studi di Catania, viale A. Doria 6, 95125 Catania
  • 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
  • Received: 01 February 2006 Accepted: 29 June 2018 Published: 01 August 2006

  • MSC : 92D30.

  • Magnetoencephalography (MEG) brain signals are studied using a method for characterizing complex nonlinear dynamics. This approach uses the value of d∞ (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∞ 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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  • Magnetoencephalography (MEG) brain signals are studied using a method for characterizing complex nonlinear dynamics. This approach uses the value of d∞ (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∞ 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.


  • This article has been cited by:

    1. M. Bucolo, F. Di Grazia, F. Sapuppo, D. Shannahoff-Khalsa, 2010, Identification of MEG-related brain dynamics induced by a yogic breathing technique, 978-1-4244-4997-2, 1, 10.1109/WHCM.2010.5441270
    2. Maide Bucolo, Federica Di Grazia, Luigi Fortuna, Mattia Frasca, Francesca Sapuppo, David Shannahoff-Khalsa, 2007, Complementary Methods for Interpreting Brain Signals: Linear versus Nonlinear Techniques, 978-1-4244-0787-3, 1969, 10.1109/IEMBS.2007.4352704
    3. Maide Bucolo, Federica Di Grazia, Mattia Frasca, Luigi Fortuna, Francesca Sapuppo, 2007, BioS: a New Tool for Biopotential Experiments, 978-1-4244-0787-3, 5190, 10.1109/IEMBS.2007.4353511
    4. Maide Bucolo, Federica Di Grazia, Francesca Sapuppo, Maria C. Virzi, 2008, A new approach for nonlinear time series characterization, “DivA”, 978-1-4244-2504-4, 1284, 10.1109/MED.2008.4602067
    5. P. Arena, A. Bonasera, C. Brigante, M. Bucolo, F. Di Grazia, D. Lombardo, F. Sapuppo, M.C. Virzi, 2008, Towards a portable device for real-time nonlinear characterization of heart dynamics, 978-1-4244-2504-4, 860, 10.1109/MED.2008.4602134
    6. Thippa Reddy Gadekallu, Neelu Khare, Sweta Bhattacharya, Saurabh Singh, Praveen Kumar Reddy Maddikunta, In-Ho Ra, Mamoun Alazab, Early Detection of Diabetic Retinopathy Using PCA-Firefly Based Deep Learning Model, 2020, 9, 2079-9292, 274, 10.3390/electronics9020274
    7. P. Arena, M. Bucolo, L. Fortuna, M. Frasca, M La Rosa, F. Sapuppo, E. Umana, D. Shannahoff-Khalsa, 2007, d-infinite Criteria for MEG Characterization, 1-4244-0920-9, 1317, 10.1109/ISCAS.2007.378414
    8. Maide Bucolo, Federica Di Grazia, Mattia Frasca, Francesca Sapuppo, David Shannahoff-Khalsa, 2008, From synchronization to network theory: A strategy for MEG data analysis, 978-1-4244-2504-4, 854, 10.1109/MED.2008.4602069
    9. M. Bucolo, R. Caponetto, G. Dongola, A. Gallo, F. Sapuppo, 2010, An FPGA based approach for nonlinear characterization of Electrocardiographic data, 978-1-4244-6390-9, 1567, 10.1109/ISIE.2010.5636316
    10. S. Rinesh, Mahdi Ismael Omar, K. Thamaraiselvi, V. Karthick, Vigneshwar Manoharan, 2023, 9781119760467, 69, 10.1002/9781119763468.ch4
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  • © 2006 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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