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.
Gupta
L
L
Department of Electrical Engineering, Southern Illinois University at Carbondale, USA.
Molfese
D L
DL
Tammana
R
R
D001921:Brain / Q000503:physiopathology*; D001930:Brain Injuries / Q000503:physiopathology; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000503:physiopathology; D004569:Electroencephalography / Q000145:classification*; D005072:Evoked Potentials, Auditory / Q000502:physiology; D005074:Evoked Potentials, Visual / Q000502:physiology; D006801:Humans; D016571:Neural Networks, Computer; D011930:Reaction Time / Q000502:physiology; D012815:Signal Processing, Computer-Assisted