ERP signal identification of Individuals at Risk for Alcoholism using Learning Vector Quantization Network

In this work, a correlation between Event Related Potential (ERP) and visual memory, generally located in occipito-temporal region was found for two classes of subject: a sample with high risk (HR) for alcoholism and a sample of control subjects with low risk (LR). For the ERPs of matching stimulus we describe an application of an artificial neural network (ANN) algorithm proposed by Kohonen and namely Learning Vector Quantization (LVQ) for the classification of ERPs signals from individuals at HR and LR for alcoholism. After training, the LVQs nets were able to correctly classify about 80% of the HR and LR class of ERP. The results of this study suggest, as well, that the reduced amplitude of the c247 and P3 to matching stimuli appears to characterize subjects at HR for alcoholism.