Statistical Method to Extract Evoked Potentials from Noise

Evoked Potentials are induced by visual or auditory stimulation. The Evoked Potentials represent transient electrical activities of some limited brain regions. The signal-noise ratio (SNR) of the EPs is typically around -10 dB. In order to study brain activities related to information processing in the brain, one has to “extract” the single EPs from the noise. We propose a method does not require a priori information concerning the characteristics (time, frequency) of the signal and does not use a template. The method proposed in this work use the wavelet transform associated with a statistical test.

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Objective detection of brainstem auditory evoked potentials with a priori information from higher presentation levels

Artificial Intelligence in Medicine ◽

10.1016/s0933-3657(02)00029-5 ◽

2002 ◽

Vol 25 (3) ◽

pp. 283-301 ◽

Cited By ~ 35

Author(s):

Edwige Vannier ◽

Olivier Adam ◽

Jean-François Motsch

Keyword(s):

Evoked Potentials ◽

Auditory Evoked Potentials ◽

A Priori ◽

Brainstem Auditory Evoked Potentials ◽

A Priori Information ◽

Priori Information ◽

Objective Detection

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Use of a Priori Information in the Estimation of a Parameter in Life Testing

Communications in Statistics - Simulation and Computation ◽

10.1080/03610917508548346 ◽

1975 ◽

Vol 4 (2) ◽

pp. 177-184

Author(s):

S. M. Shah

Keyword(s):

A Priori ◽

Life Testing ◽

A Priori Information ◽

Priori Information

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Estimation of State and Parameters of Dynamic System with the Use of Ellipsoids at the Lack of a Priori Information on Estimated Quantities

Journal of Automation and Information Sciences ◽

10.1615/jautomatinfscien.v46.i4.50 ◽

2014 ◽

Vol 46 (4) ◽

pp. 60-75 ◽

Cited By ~ 3

Author(s):

Nikolay N. Salnikov

Keyword(s):

Dynamic System ◽

A Priori ◽

A Priori Information ◽

Priori Information

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Bayesian recognition of a moving object information-measuring system state: a priori information weight correction

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1027/1/012021 ◽

2021 ◽

Vol 1027 ◽

pp. 012021

Author(s):

S M Muzhichek ◽

G G Sebryakov ◽

V I Pavlov ◽

S V Tolstych ◽

M B Kenzhekhanova

Keyword(s):

A Priori ◽

Moving Object ◽

Measuring System ◽

System State ◽

A Priori Information ◽

Information Measuring ◽

Information Measuring System ◽

Priori Information ◽

Weight Correction

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Identifying the Permeability Field of a Three-Dimensional Layered Bed with Allowance for a Priori Information on Wells under Steady-State Single-Phase Filtration Conditions

Journal of Engineering Physics and Thermophysics ◽

10.1007/s10891-021-02332-7 ◽

2021 ◽

Author(s):

A. V. Elesin ◽

A. Sh. Kadyrova ◽

A. I. Nikiforov

Keyword(s):

Steady State ◽

Single Phase ◽

A Priori ◽

Three Dimensional ◽

A Priori Information ◽

Layered Bed ◽

Priori Information ◽

Permeability Field

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T.J. Rothenberg, Efficient Estimation with A Priori Information. London,Yale University Press, 1973, VIII p.180 p., £ 4.00.

Bulletin de l’Institut de recherches économiques ◽

10.1017/s0770451800000713 ◽

1975 ◽

Vol 41 (02) ◽

pp. 186-187

Keyword(s):

A Priori ◽

Efficient Estimation ◽

A Priori Information ◽

Yale University ◽

Priori Information

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Encoding a priori information in neural networks to improve its modeling performance under non-stationary environment

Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826) ◽

10.1109/icmlc.2004.1378559 ◽

2005 ◽

Cited By ~ 1

Author(s):

Cheng-kui Gu ◽

Zheng-ou Wang ◽

Ya-ming Sun

Keyword(s):

Neural Networks ◽

A Priori ◽

A Priori Information ◽

Priori Information

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Retrieving atmospheric temperature profiles by microwave radiometry using a priori information on atmospheric spatial-temporal evolution

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/36.951080 ◽

2001 ◽

Vol 39 (9) ◽

pp. 1896-1905 ◽

Cited By ~ 6

Author(s):

P. Basili ◽

S. Bonafoni ◽

P. Ciotti ◽

F.S. Marzano ◽

G. d'Auria ◽

...

Keyword(s):

Temporal Evolution ◽

A Priori ◽

Atmospheric Temperature ◽

Microwave Radiometry ◽

Temperature Profiles ◽

A Priori Information ◽

Priori Information

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Inverse Analysis Method Using a Set of Solutions and A Priori Information.

TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series A ◽

10.1299/kikaia.61.1651 ◽

1995 ◽

Vol 61 (587) ◽

pp. 1651-1656

Author(s):

Kenji Amaya ◽

Shigeru Aoki

Keyword(s):

Inverse Analysis ◽

A Priori ◽

Analysis Method ◽

A Priori Information ◽

Priori Information ◽

Inverse Analysis Method

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Resolution Enhancement of Composite Spectra with Fractal Noise in Derivative Spectrometry

Applied Spectroscopy ◽

10.1366/0003702001949988 ◽

2000 ◽

Vol 54 (5) ◽

pp. 721-730 ◽

Cited By ~ 12

Author(s):

S. S. Kharintsev ◽

D. I. Kamalova ◽

M. Kh. Salakhov

Keyword(s):

A Priori ◽

Resolution Enhancement ◽

Numerical Differentiation ◽

A Priori Information ◽

Experimental Ir ◽

Composite Spectra ◽

Self Similar ◽

Priori Information ◽

Fractal Noise ◽

Derivative Spectrometry

The problem of improving the resolution of composite spectra with statistically self-similar (fractal) noise is considered within the framework of derivative spectrometry. An algorithm of the numerical differentiation of an arbitrary (including fractional) order of spectra is produced by the statistical regularization method taking into account a priori information on statistical properties of the fractal noise. Fractal noise is analyzed in terms of the statistical Hurst method. The efficiency and expedience of this algorithm are exemplified by treating simulated and experimental IR spectra.

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