Coupled oscillators for modeling and analysis of EEG/MEG oscillations
-
Lutz Leistritz
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).
References
[1] Arnold M, Witte H, Schelenz C. Time-variant investigation of quadratic phase couplings caused by amplitude modulation in electroencephalic burst-suppression patterns. J Clin Monit Comput2002; 17: 115–123.Search in Google Scholar
[2] Bock HG. Numerical treatment of inverse problems in chemical reaction systems. Springer Ser Chem Phys1981; 18: 102–125.10.1007/978-3-642-68220-9_8Search in Google Scholar
[3] Cimponeriu L, Rosenblum MG, Fieseler T, et al. Inferring asymmetric relations between interacting neuronal oscillators. Prog Theor Phys Suppl2003; 150: 22–36.10.1143/PTPS.150.22Search in Google Scholar
[4] de Boer RW. Beat-to-beat blood pressure fluctuations and heart rate variability in man: physiological relationships, analysis techniques and a simple model. Ph.D thesis, University of Amsterdam 1985.Search in Google Scholar
[5] Eiselt M, Schindler J, Arnold M, Witte H, Zwiener U, Frenzel J. Functional interactions within the newborn brain investigated by adaptive coherence analysis of EEG. Neurophysiol Clin2001; 31: 104–113.10.1016/S0987-7053(01)00251-9Search in Google Scholar
[6] Glass L. Synchronization and rhythmic processes in physiology. Nature2001; 410: 277–284.10.1038/35065745Search in Google Scholar
[7] Gobbele R, Buchner H, Curio G. High-frequency (600 Hz) SEP activities originating in the subcortical and cortical human somatosensory system. Evoked potentials. Electroencephalogr Clin Neurophysiol1998; 108: 182–189.10.1016/S0168-5597(97)00100-7Search in Google Scholar
[8] Gobbele R, Waberski TD, Simon H, et al. Different origins of low- and high-frequency components (600 Hz) of human somatosensory evoked potentials. Clin Neurophysiol2004; 115: 927–937.10.1016/j.clinph.2003.11.009Search in Google Scholar PubMed
[9] Grasman J, Jansen M. Mutually synchronized relaxation oscillators as prototypes of oscillating systems in biology. J Math Biol1979; 7: 171–197.10.1007/BF00276928Search in Google Scholar
[10] Gray CM. Synchronous oscillations in neuronal systems: mechanism and functions. J Comput Neurosci1994; 1: 11–38.10.1007/BF00962716Search in Google Scholar PubMed
[11] Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by simulated annealing. Science1983; 220: 671–680.10.1126/science.220.4598.671Search in Google Scholar PubMed
[12] Leistritz L, Jäger H, Schelenz C, et al. New approaches for detection and analysis of electroencephalographic burst-suppression patterns in patients with sedation. J Clin Monit1999; 15: 357–367.10.1023/A:1009990629797Search in Google Scholar
[13] Leistritz L, Suesse T, Haueisen J, Hilgenfeld B, Witte H. Methods for parameter identification in oscillatory networks and application to cortical and thalamic 600 Hz activity. J Physiol Paris2006; 99: 58–65.10.1016/j.jphysparis.2005.06.007Search in Google Scholar
[14] Linkens D, Taylor I, Duthie H. Mathematical modeling of the colorectal myoelectric activity in humans. IEEE Trans Biomed Eng1976; 23: 101–110.10.1109/TBME.1976.324569Search in Google Scholar
[15] Marquardt D. An algorithm for least squares estimation on nonlinear parameters. SIAM J Appl Math1963; 11: 431–441.10.1137/0111030Search in Google Scholar
[16] Nielson HB. Damping parameter in Marquardt's method. Technical report IMM-REP-1999-05. Department of Mathematical Modeling, Technical University of Denmark 1999.Search in Google Scholar
[17] Sarkela M, Mustola S, Seppanen T, et al. Automatic analysis and monitoring of burst suppression in anesthesia. J Clin Monit Comput2002; 17: 125–134.Search in Google Scholar
[18] Schack B, Witte H, Helbig M, Schelenz C, Specht M. Time-variant non-linear phase-coupling analysis of EEG burst patterns in sedated patients during electroencephalic burst suppression period. Clin Neurophysiol2001; 112: 1388–1399.10.1016/S1388-2457(01)00577-6Search in Google Scholar
[19] Singer W, Gray CM. Visual feature integration and the temporal correlation hypothesis. Annu Rev Neurosci1995; 18: 555–586.10.1146/annurev.ne.18.030195.003011Search in Google Scholar
[20] Solis FJ, Wets RJB. Minimization by random search techniques. Math Operat Res1981; 6: 19–30.10.1287/moor.6.1.19Search in Google Scholar
[21] Stefanovska A, Luchinsky DG, McClintock PVE. Modelling couplings among oscillators of the cardiovascular system. Physiol Meas2001; 22: 551–564.10.1088/0967-3334/22/3/312Search in Google Scholar
[22] Wang DL. Object selection based on oscillatory correlation. Neural Netw1999; 12: 579–592.10.1016/S0893-6080(99)00028-3Search in Google Scholar
[23] Witte H, Putsche P, Eiselt M, et al. Analysis of the interrelations between a low-frequency and a high-frequency signal component in human neonatal EEG during quiet sleep. Neurosci Lett1997; 236: 175–179.10.1016/S0304-3940(97)00751-9Search in Google Scholar
[24] Witte H, Rother M. Better quantification of neonatal respiratory sinus arrhythmia-progress by modelling and model-related physiological examinations. Med Biol Eng Comput1989; 27: 289–306.10.1007/BF02441489Search in Google Scholar PubMed
[25] Witte H, Schack B, Helbig M, et al. Quantification of transient quadratic phase couplings within EEG burst patterns in sedated patients during electroencephalic burst-suppression period. J Physiol Paris2000; 94: 427–434.10.1016/S0928-4257(00)01086-XSearch in Google Scholar
[26] Witte H, Schelenz C, Specht M, et al. Interrelations between EEG frequency components in sedated intensive care patients during burst-suppression period. Neurosci Lett1999; 260: 53–56.10.1016/S0304-3940(98)00944-6Search in Google Scholar
[27] Witte H, Schelenz C, Specht M, et al. Interrelations between EEG frequency components in sedated intensive care patients during burst-suppression period. Neurosci Lett1999; 260: 53–56.10.1016/S0304-3940(98)00944-6Search in Google Scholar
©2007 by Walter de Gruyter Berlin New York
Articles in the same Issue
- Ralph Mueller and Herbert Witte join the Associate Editor team of Biomedizinische Technik/Biomedical Engineering
- Technological innovations in information engineering demand sustained updating and upgrading in biosignal processing applications: a continual renaissance
- Predicting initiation and termination of atrial fibrillation from the ECG
- Predicting the QRS complex and detecting small changes using principal component analysis
- The role of independent component analysis in the signal processing of ECG recordings
- Implantable cardioverter defibrillator algorithms: status review in terms of computational cost
- Assessment of dynamic changes in cerebral autoregulation
- Corrected body surface potential mapping
- Autonomic cardiac control in animal models of cardiovascular diseases. I. Methods of variability analysis
- Autonomic cardiac control in animal models of cardiovascular diseases II. Variability analysis in transgenic rats with α-tropomyosin mutations Asp175Asn and Glu180Gly
- Fetal ECG extraction during labor using an adaptive maternal beat subtraction technique
- Heart rate variability in the fetus: a comparison of measures
- Estimation of spontaneous baroreflex sensitivity using transfer function analysis: effects of positive pressure ventilation
- Mobile nocturnal long-term monitoring of wheezing and cough
- Vigilance monitoring – review and practical aspects
- Coupled oscillators for modeling and analysis of EEG/MEG oscillations
- Auditory evoked potentials for the assessment of depth of anaesthesia: different configurations of artefact detection algorithms
- NeuMonD: a tool for the development of new indicators of anaesthetic effect
- Recording of focal direct current (DC) changes in the human cerebral cortex using refined non-invasive DC-EEG methodology
- Comparing a template approach and complex bandpass filtering for single-trial analysis of auditory evoked M100
- Wavelet-based analysis of MMN responses in children
- Branched EMG electrodes for stable and selective recording of single motor unit potentials in humans
- EMG analysis of the thenar muscles as a model for EMG-triggered larynx stimulation
- Physiological MR signal variations within the brain at 3 T
- Application of decorrelation-independent component analysis to biomagnetic multi-channel measurements
- A method for locating gradual changes in time series
- The use of digital signal processors (DSPs) in real-time processing of multi-parametric bioelectronic signals
- Steps towards a miniaturized, robust and autonomous measurement device for the long-term monitoring of patient activity: ActiBelt
- Motor timing and more – additional options using advanced registration and evaluation of tapping data
- Cellular signaling: aspects for tumor diagnosis and therapy
- List of reviewers engaged in the Special Issues on Biosignal Processing
- Stellungnahme zu „In vitro Langzeitkultur von humanem Knochen unter physiologischen Lastbedingungen“; Biomed Tech 2004; 49: 364–367
Articles in the same Issue
- Ralph Mueller and Herbert Witte join the Associate Editor team of Biomedizinische Technik/Biomedical Engineering
- Technological innovations in information engineering demand sustained updating and upgrading in biosignal processing applications: a continual renaissance
- Predicting initiation and termination of atrial fibrillation from the ECG
- Predicting the QRS complex and detecting small changes using principal component analysis
- The role of independent component analysis in the signal processing of ECG recordings
- Implantable cardioverter defibrillator algorithms: status review in terms of computational cost
- Assessment of dynamic changes in cerebral autoregulation
- Corrected body surface potential mapping
- Autonomic cardiac control in animal models of cardiovascular diseases. I. Methods of variability analysis
- Autonomic cardiac control in animal models of cardiovascular diseases II. Variability analysis in transgenic rats with α-tropomyosin mutations Asp175Asn and Glu180Gly
- Fetal ECG extraction during labor using an adaptive maternal beat subtraction technique
- Heart rate variability in the fetus: a comparison of measures
- Estimation of spontaneous baroreflex sensitivity using transfer function analysis: effects of positive pressure ventilation
- Mobile nocturnal long-term monitoring of wheezing and cough
- Vigilance monitoring – review and practical aspects
- Coupled oscillators for modeling and analysis of EEG/MEG oscillations
- Auditory evoked potentials for the assessment of depth of anaesthesia: different configurations of artefact detection algorithms
- NeuMonD: a tool for the development of new indicators of anaesthetic effect
- Recording of focal direct current (DC) changes in the human cerebral cortex using refined non-invasive DC-EEG methodology
- Comparing a template approach and complex bandpass filtering for single-trial analysis of auditory evoked M100
- Wavelet-based analysis of MMN responses in children
- Branched EMG electrodes for stable and selective recording of single motor unit potentials in humans
- EMG analysis of the thenar muscles as a model for EMG-triggered larynx stimulation
- Physiological MR signal variations within the brain at 3 T
- Application of decorrelation-independent component analysis to biomagnetic multi-channel measurements
- A method for locating gradual changes in time series
- The use of digital signal processors (DSPs) in real-time processing of multi-parametric bioelectronic signals
- Steps towards a miniaturized, robust and autonomous measurement device for the long-term monitoring of patient activity: ActiBelt
- Motor timing and more – additional options using advanced registration and evaluation of tapping data
- Cellular signaling: aspects for tumor diagnosis and therapy
- List of reviewers engaged in the Special Issues on Biosignal Processing
- Stellungnahme zu „In vitro Langzeitkultur von humanem Knochen unter physiologischen Lastbedingungen“; Biomed Tech 2004; 49: 364–367
