Non-linear and linear forecasting of the EEG time series

Blinowska, Katarzyna J.; Malinowski, Mateusz

doi:10.1007/bf00243291

Cited by 90 publications

(64 citation statements)

References 8 publications

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“…It has yet to be extended to describe EEG coherences and phases. The Multivariate Autoregressive (MAR) Model (Rappelsberger and Petsche 1975;Gersch and Yonemoto 1977;Franaszczuk et al 1985;Blinowska and Malinowski 1991) provides a parametric spectral description of multichannel EEG signals without the disadvantages of the models mentioned above, at the expense of being computationally more timeconsuming.…”

Section: Introductionmentioning

confidence: 99%

Modeling the electroencephalogram by means of spatial spline smoothing and temporal autoregression

1995

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A spatial-temporal model for the description of electroencephalographic (EEG) data is introduced that combines smooth reconstruction in the spatial domain and autoregressive representation in the time domain. Its spatial aspect is formulated in a general framework that covers interpolation, smoothing, and regression. Contrary to the multivariate time series models used for EEG analysis up to date, the introduced model provides a smooth spatial reconstruction of the EEG cross-spectrum, keeping the condition of nonnegative definiteness. As an instance of practical importance, the case in which the spatial reconstruction is based on spherical splines is developed in detail. Illustrative examples are presented that show the flexibility of the model to describe both normal and abnormal EEG data.

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Section: Introductionmentioning

confidence: 99%

Modeling the electroencephalogram by means of spatial spline smoothing and temporal autoregression

1995

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“…Systematic differences in the dynamics may lead to very different degrees of predictability. Such a predictive model has recently been applied to time series analysis of electroencephalogram (EEG) data (21).…”

Section: Introductionmentioning

confidence: 99%

Time series prediction of plasma hormone concentration. Evidence for differences in predictability of parathyroid hormone secretion between osteoporotic patients and normal controls.

Prank

Nowlan²,

Harms³

et al. 1995

J. Clin. Invest.

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Recent evidence links osteoporosis, a disease of bone remodeling, to changes in the dynamics of parathyroid hormone secretion. We use nonlinear and linear time series prediction to characterize the secretory dynamics of parathyroid hormone in both healthy human subjects and patients with osteoporosis. Osteoporotic patients appear to lack the periods of high predictability found in normal humans. Our results may provide an explanation for why an intermittent administration of parathyroid hormone is effective in restoring bone mass in osteoporotic patients. (J. Clin. Invest. 1995Invest. . 95:2910Invest. -2919

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“…A number of other non-linear statistics have been used to investigate changes in the EEG: correlation density (LEANER, 1996;MARTINERIE et al, 1998), cross-correlation integral (LE VAN QUYEN et al, 1999;2000;2001), Lyapunov exponents IASEMIDIS et al, 1999;SACKELLARES et al, 2000), similarity measures (HIVELY, 1999) and non-linear predictability (BLINOWSKA and MALINOWSKI, 1991;HERN~NDEZ et al, 1995;CASDAGLI et aL, 1996;CASDAGLI, 1997). The fundamental link between all these non-linear statistics is that they are defined with respect to a particular state space, if the distribution of points in this state space varies, then these statistics are likely to change.…”

Section: 4 Non-linear Analysismentioning

confidence: 99%

Linear and non-linear methods for automatic seizure detection in scalp electro-encephalogram recordings

McSharry

Smith

et al. 2002

Med. Biol. Eng. Comput.

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The electro-encephalogram is a time-varying signal that measures electrical activity in the brain. A conceptually intuitive non-linear technique, multi-dimensional probability evolution (MDPE), is introduced. It is based on the time evolution of the probability density function within a multi-dimensional state space. A synthetic recording is employed to illustrate why MDPE is capable of detecting changes in the underlying dynamics that are invisible to linear statistics. If a non-linear statistic cannot outperform a simple linear statistic such as variance, then there is no reason to advocate its use. Both variance and MDPE were able to detect the seizure in each of the ten scalp EEG recordings investigated. Although MDPE produced fewer false positives, there is no firm evidence to suggest that MDPE, or any other non-linear statistic considered, outperforms variance-based methods at identifying seizures.

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Non-linear and linear forecasting of the EEG time series

Cited by 90 publications

References 8 publications

Modeling the electroencephalogram by means of spatial spline smoothing and temporal autoregression

Modeling the electroencephalogram by means of spatial spline smoothing and temporal autoregression

Time series prediction of plasma hormone concentration. Evidence for differences in predictability of parathyroid hormone secretion between osteoporotic patients and normal controls.

Linear and non-linear methods for automatic seizure detection in scalp electro-encephalogram recordings

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