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.
ASJC Scopus subject areas
- Neuropsychology and Physiological Psychology
- Experimental and Cognitive Psychology
- Developmental and Educational Psychology
- Arts and Humanities (miscellaneous)
- Cognitive Neuroscience