Neural network model of cortical EEG response to olfactory stimuli

摘要

We describe three experiments attempting to model differences in cortical EEG following stimulation with different odours. The data used in these experiments was obtained in previous studies, described briefly here. Subjects sit in an environmentally stabilised low odour 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 odour 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 odours 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 odour. Generalisation was demonstrated for five other odours. The weights in the network have been analysed 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 odours. In the original experiment from which we drew our data, fourteen subjects were presented with each odour once. Data from only the frame at fist presentation of the odour were used here. Data from three subjects (chosen pseudo-randomly) was selected for use in the generalisation 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.