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Effects of Input Neuron Mapping Coordinates in Spiking Neural Network on the Motor Imagery EEG Signals Classification | IEEE Conference Publication | IEEE Xplore

Effects of Input Neuron Mapping Coordinates in Spiking Neural Network on the Motor Imagery EEG Signals Classification


Abstract:

Spiking neural network (SNN), the third generation of the artificial neural network, uses the same computational principles as Spatio-temporal brain data (STBD) generatio...Show More

Abstract:

Spiking neural network (SNN), the third generation of the artificial neural network, uses the same computational principles as Spatio-temporal brain data (STBD) generation, so it can be used as an effective tool to learn and understand STBD. When using SNN to process STBD, mapping of input neurons is a problem to be considered. This paper investigates the influence of changing the mapping coordinates of input neurons on motor imagery EEG signal classification. The results show that the coordinate change affects the interaction between the input neuron and its neighboring neurons, and causes the classification accuracy to change, but the impact mechanism is unknown.
Date of Conference: 20-22 February 2023
Date Added to IEEE Xplore: 28 March 2023
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Conference Location: Gangwon, Korea, Republic of

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