AR spectral analysis of EEG signals by using maximum likelihood estimation

Güler, İnan; Kıymık, M. Kemal; Akın, Mehmet; Alkan, Ahmet

doi:10.1016/s0010-4825(01)00022-1

Cited by 94 publications

(46 citation statements)

References 5 publications

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“…The Fourier transform, however, has some disadvantages for dealing with non-stationary EEG signals. Therefore, other parametric and non-parametric spectral estimation methods have been proposed for EEG signal analysis (Gersch and Yonemoto, 1977;Isaksson, 1981;Pascualmarqui et al, 1988;Tseng et al, 1995;Pardey et al, 1996;Muthuswamy and Thakor, 1998;Quiroga et al, 1997, Guler et al, 2001Panzica et al, 2003;Subasi, 2007;Zhou et al, 2008).…”

Section: Electroencephalography (Eeg) Is An Important Non-invasive Tementioning

confidence: 99%

Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multiwavelets

Wei

Billings

Liu

2010

IJMIC

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Abstract:A new time-varying autoregressive (TVAR) modelling approach is proposed for nonstationary signal processing and analysis, with application to EEG data modelling and power spectral estimation. In the new parametric modelling framework, the time-dependent coefficients of the TVAR model are represented using a novel multi-wavelet decomposition scheme. The timevarying modelling problem is then reduced to regression selection and parameter estimation, which can be effectively resolved by using a forward orthogonal regression algorithm. Two examples, one for an artificial signal and another for an EEG signal, are given to show the effectiveness and applicability of the new TVAR modelling method.

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Section: Electroencephalography (Eeg) Is An Important Non-invasive Tementioning

confidence: 99%

Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multiwavelets

Wei

Billings

Liu

2010

IJMIC

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“…In general, it must be said that no time-frequency regions but rather time-scale regions are defined [9], [10]. All wavelet transforms can be specified in terms of a low-pass filter g, which satisfies the standard quadrature mirror filter condition (1) Where G(z) denotes the z-transform of the filter g. Its complementary high-pass filter can be defined as (2) A sequence of filters with increasing length (indexed by i) can be obtained (3) with the initial condition G 0 (z) = 1. It is expressed as a twoscale relation in time domain (4) where the subscript [.…”

Section: Analysis Using Dwtmentioning

confidence: 99%

“…Routine clinical diagnosis needs to analysis of EEG signals. Therefore, some automation and computer techniques have been used for this aim [1]. Since the early days of automatic EEG processing, representations based on a Fourier transform have been most commonly applied.…”

Section: Introductionmentioning

confidence: 99%

EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks

Jahankhani

Kodogiannis

Revett

2006

IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA'06)

241

113

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Decision Support Systems have been utilised since 1960, providing physicians with fast and accurate means towards more accurate diagnoses and increased tolerance when handling missing or incomplete data. This paper describes the application of neural network models for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: initially, a feature extraction scheme using the wavelet transform (WT) has been applied and then a learning-based algorithm classifier performed the classification. The performance of the neural model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals.

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“…In general, it must be said that no time-frequency regions but rather time-scale regions are defined [10]. All wavelet transforms can be specified in terms of a low-pass filter g, which satisfies the standard quadrature mirror filter condition (1) where G(z) denotes the z-transform of the filter g. Its complementary high-pass filter can be defined as (2) A sequence of filters with increasing length (indexed by i) can be obtained (3) with the initial condition G 0 (z) = 1. It is expressed as a twoscale relation in time domain (4) where the subscript [.…”

Section: Analysis Using Dwtmentioning

confidence: 99%