DocumentCode :
1908136
Title :
Feature extraction of event-related potential waveforms by neural networks
Author :
Wu, Fred Y. ; Slater, Jeremy D. ; Ramsay, R. Eugene ; Honig, Lawrence S.
Author_Institution :
Dept. of Electr. & Comput. Eng., Miami Univ., Coral Gables, FL, USA
fYear :
1993
fDate :
1993
Firstpage :
1532
Abstract :
Artificial neural network (ANN) Methods may be useful in brain signal analysis, in which the signal characteristics are unknown and signal-to-noise ratios are well below one. The development of a neural network for classifying event-related potential data obtained from normal control subjects and from patients with multiple sclerosis is described. The classification strategy is then decoded by network analysis and compared with that obtained statistically. The network decision-making process is illustrated by three examples, showing the variation of the responses of internal hidden units to different input stimuli
Keywords :
bioelectric potentials; decoding; feature extraction; medical diagnostic computing; medical signal processing; neural nets; S/N ratio; brain signal analysis; decision-making process; event-related potential waveforms; feature extraction; internal hidden units; medical diagnostic computing; multiple sclerosis; neural networks; pattern recognition; Artificial neural networks; Biological neural networks; Delay; Enterprise resource planning; Feature extraction; Multiple sclerosis; Nervous system; Neural networks; Signal processing; Signal to noise ratio;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1993., IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0999-5
Type :
conf
DOI :
10.1109/ICNN.1993.298784
Filename :
298784
Link To Document :
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