d-infinite Criteria for MEG Characterization

Magneto encephalographic (MEG) brain signals are studied using a method for characterizing nonlinear dynamics. This approach uses the value of d_infin (d-infinite) to characterize the system´s asymptotic chaotic behavior. A novel procedure was developed to extract this parameter from time series when the system´s structure and laws are unknown. The implementation of the algorithm has 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, which are relative to spatio-temporal distributions of the calculated dinfin on the MEG channels, are analyzed and compared during different phases of the yogic protocol.