Visual evoked potentials discrimination based on adaptive zero-tracking neural network.
A non-linear classifier is proposed to discriminate visual evoked potentials (VEP). It combines two techniques: the zero-tracking method and a multi-layer network. The first method consists of processing the VEP data through an adaptive linear prediction filter aiming at extracting the appropriate feature vector to be fed into the neural network. 105 VEPs collected from 48 healthy people and 57 patients are analysed to test the performances of the proposed classifier. The results obtained with a back-propagation network revealed a total success rate equal to 89%. It is also found more accurate than the latency method used in hospitals.
Mghari
A
A
Département de Physique, Université My Ismail faculté des Sciences et Techniques, Boutalamine, Errachidia BP 509, Morocco. a.mghari@hotmail.com
Himmi
M M
MM
Amaloud
A
A
Regragui
F
F
D000465:Algorithms; D002493:Central Nervous System Diseases / Q000175:diagnosis; D016002:Discriminant Analysis; D005074:Evoked Potentials, Visual; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer