Identification of hemifield single trial PVEP on the basis of generalized dynamic neural network classifiers
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[1] H. Witte,et al. Identification of the stimulated hemiretinae using a reduced number of PVEP trials. , 1998, Electroencephalography and clinical neurophysiology.
[2] Miroslaw Galicki,et al. The Planning of Robotic Optimal Motions in the Presence of Obstacles , 1998, Int. J. Robotics Res..
[3] A. Ademoglu,et al. Analysis of pattern reversal visual evoked potentials (PRVEPs) by spline wavelets , 1997, IEEE Transactions on Biomedical Engineering.
[4] A Bezerianos,et al. Robust moving averages, with Hopfield neural network implementation, for monitoring evoked potential signals. , 1997, Electroencephalography and clinical neurophysiology.
[5] D Lehmann,et al. Instantaneous frequency maps, dipole models and potential distributions of pattern reversal-evoked potential fields for correct recognition of stimulated hemiretinae. , 1996, Electroencephalography and clinical neurophysiology.
[6] N Nakasato,et al. Neuromagnetic evidence that the P100 component of the pattern reversal visual evoked response originates in the bottom of the calcarine fissure. , 1996, Electroencephalography and clinical neurophysiology.
[7] J. Achimowicz,et al. Variability analysis of visual evoked potentials in humans by pattern recognition in phase domain. , 1995, Acta neurobiologiae experimentalis.
[8] Marios M. Polycarpou,et al. High-order neural network structures for identification of dynamical systems , 1995, IEEE Trans. Neural Networks.
[9] B H Jansen,et al. Selective Averaging of Evoked Potentials using Trajectory-Based Clustering , 1994, Methods of Information in Medicine.
[10] G G Celesia,et al. Identification of the hemisphere activated by hemifield visual stimulation using a single equivalent dipole model. , 1993, Electroencephalography and clinical neurophysiology.
[11] Russell Reed,et al. Pruning algorithms-a survey , 1993, IEEE Trans. Neural Networks.
[12] Jocelyn Sietsma,et al. Creating artificial neural networks that generalize , 1991, Neural Networks.
[13] R. V. Dooren,et al. A Chebyshev technique for solving nonlinear optimal control problems , 1988 .
[14] PD Thompson. A Textbook of Clinical Neurophysiology , 1988 .
[15] C. E. Wright,et al. Visual evoked potentials in acute optic neuritis , 1985 .
[16] J Möcks,et al. Variability of single visual evoked potentials evaluated by two new statistical tests. , 1984, Electroencephalography and clinical neurophysiology.
[17] D. Lehmann,et al. Reference-free identification of components of checkerboard-evoked multichannel potential fields. , 1980, Electroencephalography and clinical neurophysiology.
[18] G. Barrett,et al. A paradox in the lateralisation of the visual evoked response , 1976, Nature.
[19] Herbert Witte,et al. Training Continuous Trajectories by Means of Dynamic Neural Networks with Time Dependent Weights , 1998, NC.
[20] M. Jobert,et al. Processing visual evoked potentials based on matched filtering of single trial responses. , 1996, Neuropsychobiology.
[21] F. K. Lam,et al. Visual evoked potential enhancement by an artificial neural network filter. , 1996, Bio-medical materials and engineering.
[22] M H Cuypers,et al. Improving the ensemble average of visual evoked potentials. II. Simulations and experiments. , 1995, Technology and health care : official journal of the European Society for Engineering and Medicine.
[23] F. K. Lam,et al. Visual evoked potential measurement by adaptive filtering. , 1994, Bio-medical materials and engineering.
[24] F. Mauguière,et al. Clinical applications of evoked potentials in neurology , 1982 .
[25] D Lehmann,et al. Intracerebral and scalp fields evoked by hemiretinal checkerboard reversal, and modeling of their dipole generators. , 1982, Advances in neurology.