R Tammana | PubMed Health

PubMed1996

Nonlinear alignment and averaging for estimating the evoked potential.

This paper addresses the problems associated with averaging brain responses evoked through a repetitive application of an external stimulus. In order to improve the estimate of the evoked potential (EP) through signal averaging, a method which incorporates nonlinear alignment of the EP's into the averaging operation is developed. The method makes no prior assumptions about the properties of the EP or which response in the set best characterizes the EP to be estimated. The nonlinear alignment procedure is designed to pairwise generate optimally aligned EP's by backtracking along the optimal alignment path. The nonlinear alignment and averaging operations are systematically combined to develop methods to estimate the EP. Results from a series of experiments conducted on simulated and real sets of responses show that, through nonlinear alignment and averaging, the events in the EP's are preserved and the estimates of the EP are quite robust.

Adult↗

PubMed1995

An artificial neural network approach to ERP classification.

Artificial neural network-based approaches were developed to classify event-related potential (ERP) waveforms. The networks utilized scalp-recorded ERP returns from six electrode sites. These ERPs were evoked as one individual responded to a series of auditorily presented object names while viewing various objects on a computer screen. The ERPs at the electrode sites were classified as a match decision or a no-match decision. A three-layer backpropagation neural network model was selected to formulate a global and a local classification approach. The backpropagation network in the global approach was designed to operate on a single ERP response which was the average of the ERP responses generated at the six electrode sites. The local ERP classification system consisted of six three-layer backpropagation networks. Each network was designed to operate on the ERPs generated at a single electrode site. A small data base consisting of eight match and eight no-match ERP responses was used to train and test the networks in a variety of ways. The results obtained clearly show that the neural network-based classifiers are able to discriminate with a high degree of accuracy between match and no-match conditions in ERP waveforms.

Brain↗