Complex spatio-temporal features in meg data - Francesca Sapuppo; Elena Umana; Mattia Frasca; Manuela La Rosa; David Shannahoff-Khalsa; Luigi Fortuna; Maide Bucolo (Mathematical Biosciences and Engineering)
Magnetoencephalography (MEG) brain signals are studied usinga method for characterizing complex nonlinear dynamics. This approach usesthe value of $d_∞$ (d-infinite) to characterize the system’s asymptotic chaoticbehavior. A novel procedure has been developed to extract this parameterfrom time series when the system’s structure and laws are unknown. The implementationof the algorithm was proven to be general and computationallyefficient. The information characterized by this parameter is furthermore independentand complementary to the signal power since it considers signalsnormalized with respect to their amplitude. The algorithm implemented hereis applied to whole-head 148 channel MEG data during two highly structuredyogic breathing meditation techniques. Results are presented for the spatiotemporaldistributions of the calculated $d_∞$ on the MEG channels, and theyare compared for the different phases of the yogic protocol. The algorithm wasapplied to six MEG data sets recorded over a three-month period. This providesthe opportunity of verifying the consistency of unique spatio-temporalfeatures found in specific protocol phases and the chance to investigate thepotential long term effects of these yogic techniques. Differences among thespatio-temporal patterns related to each phase were found, and they wereindependent of the power spatio-temporal distributions that are based on conventionalanalysis. This approach also provides an opportunity to compareboth methods and possibly gain complementary information.