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Proceedings Paper

Neural network model of cortical EEG response to olfactory stimuli
Author(s): George L. Dunbar; Steve Van Toller
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Paper Abstract

We describe three experiments attempting to model differences in cortical EEG following stimulation with different odors. The data used in these experiments was obtained in previous studies, described briefly here. Subjects sit in an environmentally stabilized low odor cubicle. Twenty-eight electrodes are placed on the scalp and connect the subject to a neurosciences brain imager, which digitizes cortical EEG response. In a given trial, a specific odor is introduced, and the response recorded. In the first experiment, alpha wave data from a subset of ten electrodes and a single subject was used. In the original experiment, the subject was presented with a number of odors and the resulting brain electrical activity was resolved into 16 time slices (5 preceding presentation, 4 during presentation and 7 following presentation). Only data from frames 6, 7 and 8 (during presentation) was used here. A model was constructed to discriminate morning from afternoon responses. The network used measurements from 10 electrodes as input, and backpropagation was used for training. During training, the network was presented with responses to just one odor. Generalization was demonstrated for five other odors. The weights in the network have been analyzed and indicate a role for a specific group of electrode sites in this discrimination. The second experiment involved constructing a network to discriminate cortical EEG responses to two odors. In the original experiment from which we drew our data, fourteen subjects were presented with each odor once. Data from only the frame at first presentation of the odor were used here. Data from three subjects (chosen pseudo-randomly) was selected for use in the generalization phase and dropped from the training set. Output targets were constructed that took account of subjective ratings of `pleasantness.' A feed-forward network with twenty-eight input units was trained using data from the eleven remaining subjects, using conjugate gradient descent.

Paper Details

Date Published: 6 April 1995
PDF: 7 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205129
Show Author Affiliations
George L. Dunbar, Univ. of Warwick (United Kingdom)
Steve Van Toller, Univ. of Warwick (United Kingdom)

Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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