title: Identification of hemifield single trial PVEP on the basis of generalized dynamic neural network classifiers.
abstract: This paper is concerned with the application of generalized dynamic neural networks for the identification of hemifield pattern-reversal visual evoked potentials. The identification process is performed by different networks with time-varying weights using signals from different electrode positions as external inputs. Since dynamic neural networks are able to process time-varying signals, the identification of the stimulated hemiretinae is performed without feature extraction. The performance of the method presented is compared with a reference method based on the values of instantaneous frequency at the occipital electrode positions at P100 latency.
authors.affiliation: Institute of Medical Statistics, Computer Sciences and Documentation Friedrich-Schiller-University, Jena, Germany. i6lelu@imsid.uni-jena.de
authors.forename: L
authors.initials: L
authors.lastname: Leistritz
authors.forename: K
authors.initials: K
authors.lastname: Hoffmann
authors.forename: M
authors.initials: M
authors.lastname: Galicki
authors.forename: H
authors.initials: H
authors.lastname: Witte
mesh_terms: D000328:Adult; D000465:Algorithms; D003627:Data Interpretation, Statistical; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D007839:Functional Laterality / Q000502:physiology*; D006801:Humans; D008297:Male; D008959:Models, Neurological; D016571:Neural Networks, Computer; D010364:Pattern Recognition, Visual / Q000502:physiology*