Coupled oscillators for modeling and analysis of EEG/MEG oscillations

Abstract This study presents three EEG/MEG applications in which the modeling of oscillatory signal components offers complementary analysis and an improved explanation of the underlying generator structures. Coupled oscillator networks were used for modeling. Parameters of the corresponding ordinary coupled differential equation (ODE) system are identified using EEG/MEG data and the resulting solution yields the modeled signals. This model-related analysis strategy provides information about the coupling quantity and quality between signal components (example 1, neonatal EEG during quiet sleep), allows identification of the possible contribution of hidden generator structures (example 2, 600-Hz MEG oscillations in somatosensory evoked magnetic fields), and can explain complex signal characteristics such as amplitude-frequency coupling and frequency entrainment (example 3, EEG burst patterns in sedated patients).

[1]  L. Leistritz,et al.  Methods for Parameter Identification in Oscillatory Networks , 2004 .

[2]  G Curio,et al.  Different origins of low- and high-frequency components (600 Hz) of human somatosensory evoked potentials , 2004, Clinical Neurophysiology.

[3]  P. Morosan,et al.  Inferring Asymmetric Relations between Interacting Neuronal Oscillators , 2003 .

[4]  Herbert Witte,et al.  Time-Variant Investigation of Quadratic Phase Couplings Caused by Amplitude Modulation in Electroencephalic Burst-Suppression Patterns , 2002, Journal of Clinical Monitoring and Computing.

[5]  Tapio Seppänen,et al.  Automatic Analysis and Monitoring of Burst Suppression in Anesthesia , 2002, Journal of Clinical Monitoring and Computing.

[6]  H. Witte,et al.  Time-variant non-linear phase-coupling analysis of EEG burst patterns in sedated patients during electroencephalic burst suppression period , 2001, Clinical Neurophysiology.

[7]  A Stefanovska,et al.  Modelling couplings among the oscillators of the cardiovascular system , 2001, Physiological measurement.

[8]  H. Witte,et al.  Functional interactions within the newborn brain investigated by adaptive coherence analysis of EEG , 2001, Neurophysiologie Clinique/Clinical Neurophysiology.

[9]  L. Glass Synchronization and rhythmic processes in physiology , 2001, Nature.

[10]  H. Witte,et al.  Quantification of transient quadratic phase couplings within EEG burst patterns in sedated patients during electroencephalic burst-suppression period , 2000, Journal of Physiology-Paris.

[11]  H. Witte,et al.  New Approaches for the Detection and Analysis of Electroencephalographic Burst-Suppression Patterns in Patients under Sedation , 1999, Journal of Clinical Monitoring and Computing.

[12]  DeLiang Wang,et al.  Object selection based on oscillatory correlation , 1999, Neural Networks.

[13]  M. Arnold,et al.  Interrelations between EEG frequency components in sedated intensive care patients during burst-suppression period , 1999, Neuroscience Letters.

[14]  P Putsche,et al.  Analysis of the interrelations between a low-frequency and a high-frequency signal component in human neonatal EEG during quiet sleep , 1997, Neuroscience Letters.

[15]  G Curio,et al.  High-frequency (600 Hz) SEP activities originating in the subcortical and cortical human somatosensory system. , 1997, Electroencephalography and clinical neurophysiology.

[16]  Charles M. Gray,et al.  Synchronous oscillations in neuronal systems: Mechanisms and functions , 1994, Journal of Computational Neuroscience.

[17]  M. Hallett,et al.  Human somatosensory evoked potentials and volition act , 1989 .

[18]  H. Witte,et al.  Better quantification of neonatal respiratory sinus arrhythmia—progress by modelling and model-related physiological examinations , 1989, Medical and Biological Engineering and Computing.

[19]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[20]  Roger J.-B. Wets,et al.  Minimization by Random Search Techniques , 1981, Math. Oper. Res..

[21]  Derek A. Linkens,et al.  Mathematical Modeling of the Colorectal Myoelectrical Activity in Humans , 1976, IEEE Transactions on Biomedical Engineering.

[22]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[23]  H. B. Nielsen DAMPING PARAMETER IN MARQUARDT ’ S METHOD , 1999 .

[24]  W Singer,et al.  Visual feature integration and the temporal correlation hypothesis. , 1995, Annual review of neuroscience.

[25]  R. D. Boer,et al.  Beat-to-beat blood-pressure fluctuations and heart-rate variability in man: physiological relationships, analysis techniques and a simple model , 1985 .

[26]  H. Bock Numerical Treatment of Inverse Problems in Chemical Reaction Kinetics , 1981 .

[27]  Johan Grasman,et al.  Mutually synchronized relaxation oscillators as prototypes of oscillating systems in biology , 1979 .