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Methods Inf Med 2004; 43(01): 89-93
DOI: 10.1055/s-0038-1633842
Original Article
Schattauer GmbH

A Nonlinear Circuit Architecture for Magnetoencephalographic Signal Analysis

M. Bucolo
1   Dipartimento Elettrico Elettronico e Sistemistico, Università degli Studi di Catania,Catania, Italy
,
L. Fortuna
1   Dipartimento Elettrico Elettronico e Sistemistico, Università degli Studi di Catania,Catania, Italy
,
M. Frasca
1   Dipartimento Elettrico Elettronico e Sistemistico, Università degli Studi di Catania,Catania, Italy
,
M. La Rosa
1   Dipartimento Elettrico Elettronico e Sistemistico, Università degli Studi di Catania,Catania, Italy
,
M. C. Virzì
1   Dipartimento Elettrico Elettronico e Sistemistico, Università degli Studi di Catania,Catania, Italy
,
D. Shannahoff-Khalsa
2   Institute for Nonlinear Science, University of California, San Diego, La Jolla, California, USA
› Author Affiliations
Further Information

Publication History

Publication Date:
07 February 2018 (online)

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Summary

Objectives: The objective of this paper was to face the complex spatio-temporal dynamics shown by Magnetoencephalography (MEG) data by applying a nonlinear distributed approach for the Blind Sources Separation. The effort was to characterize and differentiate the phases of a yogic respiratory exercise used in the treatment of obsessive compulsive disorders.

Methods: The patient performed a precise respiratory protocol, at one breath per minute for 31 minutes, with 10 minutes resting phase before and after. The two steps of classical Independent Component Approach have been performed by using a Cellular Neural Network with two sets of templates. The choice of the couple of suitable templates has been carried out using genetic algorithm optimization techniques.

Results: Performing BSS with a nonlinear distributed approach, the outputs of the CNN have been compared to the ICA ones. In all the protocol phases, the main components founded with CNN have similar trends compared with that ones obtained with ICA. Moreover, using this distributed approach, a spatial location has been associated to each component.

Conclusions: To underline the spatio-temporal and the nonlinearly of the neural process a distributed nonlinear architecture has been proposed. This strategy has been designed in order to overcome the hypothesis of linear combination among the sources signals, that is characteristic of the ICA approach, taking advantage of the spatial information.

Keywords

Cellular Neural Network (CNN) - magnetoencephalography - blind sources separation
 

  • References

  • 1 Baillet S, Mosher JC, Leahy RM. Electromagnetic Brain Mapping. IEEE Signal Processing 2001; 18 (06) 14-30.
    CrossrefPubMedSearch in Google Scholar
  • 2 Fortuna L, Bucolo M, Frasca M, La Rosa M, Shannahoff-Khalsa DS, Schult RL, Wright JA. Independent Component Analysis of Magnetoencephalography Data. Proceedings EMBC. Istanbul (Turkey): 2001
    Search in Google Scholar
  • 3 Jung TP, Makeig S, McKeown MJ, Bell AJ, Lee TW, Sejnowkski TJ. Imaging brain dynamics using Independent Component Analysis. IEEE Proceedings, in press.
    PubMed
  • 4 Hyvarinen A, Oja E. Independent component analysis: algorithms and applications. Neural Networks 2000; 13: 411-30.
    CrossrefPubMedSearch in Google Scholar
  • 5 Chua LO, Yang L. Cellular Neural Networks: Theory. IEEE Trans. on Circuits and Systems 1988; 35: 1257-72.
    CrossrefPubMedSearch in Google Scholar
  • 6 Shannahoff-Khalsa D, Ray LE, Levine S, Gallen CC, Schwartz BJ, Sidorowich JJ. Randomized Controlled Trial of Yogic Meditation Techniques for patients with Obsessive Compulsive Disorders. CNS Spectrum: Int J Neuropsychiatric Medicine 1999; 4 (12) 34-46.
    PubMedSearch in Google Scholar