scholarly journals Adaptive neural network filter for visual evoked potential estimation

2002 ◽  
Author(s):  
K.S.M. Fung ◽  
F.K. Lam ◽  
F.H.Y. Chan ◽  
P.W.F. Poon ◽  
J.G. Lin
Keyword(s):  
Neural Network ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Adaptive Neural Network ◽  
Evoked Potential Estimation ◽  
Visual Evoked

Visual evoked potential estimation of visual activity with a Laplacian derivation

1992 ◽  
Vol 79 (4) ◽  
pp. 383-389 ◽  
Author(s):  
Alexander P. A. Beers ◽  
Frans C. C. Riemslag ◽  
Henk Spekreijse
Keyword(s):  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Visual Activity ◽  
Evoked Potential Estimation ◽  
Laplacian Derivation ◽  
Visual Evoked

scholarly journals Automatic Discrimination of Abnormal Subjects Using the Visual Evoked Potential Spectral Components

2004 ◽  
Vol 2004 (1) ◽  
pp. 5-9 ◽  
Author(s):  
R. Sivakumar ◽  
G. Ravindran
Keyword(s):  
Neural Network ◽  
Neurological Disorders ◽  
Software Package ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Computational Approach ◽  
Feedforward Neural Network ◽  
Output Vector ◽  
Spectral Components ◽  
Visual Evoked

Study of visual evoked potential (VEP) is one of the utilized methods in clinical diagnosis of ophthalmology and neurological disorders. The automatic detection of VEP spectral components is an important tool in the diagnosis of mental activity. This paper presents a novel computational approach using feedforward neural network to identify abnormal subjects from changes in spectral components. The output vector from the feedforward neural network is based on the VEP spectral components. The software was developed to identify mental state from the VEP spectral components using Matlab software package. Using this approach, it is possible to perform real-time abnormality identification accurately on personal computers.

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