Neural network classification of EEG using chaotic preprocessing and phase space reconstruction

Tumey, D.M.; Morton, Paul; Ingle, D.F.; Downey, C.W.; Schnurer, J.H.

doi:10.1109/nebc.1991.154576

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…Various methods of estimating dimension have been proposed, such as correlation dimension, 8 minimum phase space volume, 9 and box counting. 7 Density estimation techniques are typically histogram 10,11 or GMM 12 based. Topological analysis techniques include templates 13 and global vector field reconstruction.…”

Section: Previous Workmentioning

confidence: 99%

Statistical models of reconstructed phase spaces for signal classification

Povinelli

Johnson

Lindgren

et al. 2006

IEEE Trans. Signal Process.

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This paper introduces a novel approach to the analysis and classification of time series signals using statistical models of reconstructed phase spaces. With sufficient dimension, such reconstructed phase spaces are, with probability one, guaranteed to be topologically equivalent to the state dynamics of the generating system, and, therefore, may contain information that is absent in analysis and classification methods rooted in linear assumptions. Parametric and nonparametric distributions are introduced as statistical representations over the multidimensional reconstructed phase space, with classification accomplished through methods such as Bayes maximum likelihood and artificial neural networks (ANNs). The technique is demonstrated on heart arrhythmia classification and speech recognition. This new approach is shown to be a viable and effective alternative to traditional signal classification approaches, particularly for signals with strong nonlinear characteristics.

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Section: Previous Workmentioning

confidence: 99%

Statistical models of reconstructed phase spaces for signal classification

Povinelli

Johnson

Lindgren

et al. 2006

IEEE Trans. Signal Process.

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show abstract

“…The significance of regarding EEG as chaotic is that we can use a deterministic nonlinear dynamic system to accurately model the EEG signals. Several dynamical reconstruction techniques have been developed for modeling the EEG and other biomedical signals [5], [6]. In practical applications, the model trained by observed signals can be applied to identify abnormal states of the system by detecting abrupt changes in the prediction error of the ongoing signal.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Approach for Multichannel EEG Signals Modeling Using Local Wavelet SVM

Shen

Lin

Chen

et al. 2010

IEEE Trans. Instrum. Meas.

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Accurate modeling of the multichannel electroencephalogram (EEG) signal is an important issue in clinical practice. In this paper, we propose a new local spatiotemporal prediction method based on support vector machines (SVMs). Combining with the local prediction method, the sequential minimal optimization (SMO) training algorithm, and the wavelet kernel function, a local SMO-wavelet SVM (WSVM) prediction model is developed to enhance the efficiency, effectiveness, and universal approximation capability of the prediction model. Both the spatiotemporal modeling from the measured time series and the details of the nonlinear modeling procedures are discussed. Simulations and experimental results with real EEG signals show that the proposed method is suitable for real signal processing and is effective in modeling the local spatiotemporal dynamics. This method greatly increases the computational speed and more effectively captures the local information of the signal.

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Neural Networks and Their Applications

Clothiaux

Bachmann

1994

The GeoJournal Library

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Neural network classification of EEG using chaotic preprocessing and phase space reconstruction

Cited by 5 publications

References 3 publications

Statistical models of reconstructed phase spaces for signal classification

Statistical models of reconstructed phase spaces for signal classification

A Prediction Approach for Multichannel EEG Signals Modeling Using Local Wavelet SVM

Neural Networks and Their Applications

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