Investigation of the dynamics underlying periodic complexes in the EEG
| Autor: | J. H. R. Vliegen, J. Nicolai, C. J. Stam |
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| Přispěvatelé: | Neurology, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Neurodegeneration |
| Jazyk: | angličtina |
| Rok vydání: | 1999 |
| Předmět: |
Male
Correlation dimension Periodicity General Computer Science media_common.quotation_subject Models Neurological Electroencephalography Asymmetry Feedback Limit cycle Attractor medicine Calculus Humans Statistical physics media_common Mathematics Cerebral Cortex Brain Diseases medicine.diagnostic_test Series (mathematics) Nonlinear system Amplitude Nonlinear Dynamics Acute Disease Female Nerve Net Cybernetics Biotechnology |
| Zdroj: | Stam, C J, Vliegen, J H & Nicolai, J 1999, ' Investigation of the dynamics underlying periodic complexes in the EEG ', Biological cybernetics, vol. 80, no. 1, pp. 57-69 . https://doi.org/10.1007/s004220050504 Biological cybernetics, 80(1), 57-69. Springer Verlag |
| ISSN: | 0340-1200 |
| DOI: | 10.1007/s004220050504 |
| Popis: | Periodic complexes (PC), occurring lateralised or diffuse, are relatively rare EEG phenomena which reflect acute severe brain disease. The pathophysiology is still incompletely understood. One hypothesis suggested by the alpha rhythm model of Lopes da Silva is that periodic complexes reflect limit cycle dynamics of cortical networks caused by excessive excitatory feedback. We examined this hypothesis by applying a recently developed technique to EEGs displaying periodic complexes and to periodic complexes generated by the model. The technique, non-linear cross prediction, characterises how well a time series can be predicted, and how much amplitude and time asymmetry is present. Amplitude and time asymmetry are indications of non-linearity. In accordance with the model, most EEG channels with PC showed clear evidence of amplitude and time asymmetry, pointing to non-linear dynamics. However, the non-linear predictability of true PC was substantially lower than that of PC generated by the model. Furthermore, no finite value for the correlation dimension could be obtained for the real EEG data, whereas the model time series had a dimension slighter higher than one, consistent with a limit cycle attractor. Thus we can conclude that PC reflect non-linear dynamics, but a limit cycle attractor is too simple an explanation. The possibility of more complex (high dimensional and spatio-temporal) non-linear dynamics should be investigated. |
| Databáze: | OpenAIRE |
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