EEG Pattern Recognition using Brain-Inspired Spiking Neural Networks for Modelling Human Decision Processes

This paper proposes a method utilising spiking neural networks (SNN) for modelling, visualising and comparing the brain data under complex mental states. The method was applied to a cognitive task performed by 23 participants while they were making decision on a moral dilemma situation-related task. An SNN evolving spatiotemporal data architecture is used to learn and visualise the neural activity across different brain regions. The model developed allows for studying the patterns of electrical activity of neurons elicited during complex decision making processes such as moral-related tasks. This could be used for predictive analysis of various aspects of human behavior during decision making and for other related cognitive tasks.

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