Exploring the Intersection of Artificial Intelligence and Neuroscience: Heuristics for the Inference of Emotional Signals through Brain-Computer Interfaces

Abstract

This research article outlines the process of heuristic generation through generative artificial intelligence for identifying emotional signal indicators in the context of brain signals captured by brain-computer interfaces. The methodology of the work is structured in key phases, starting with the study design to establish a robust framework. Through a comprehensive theoretical analysis, the conceptual and theoretical foundations instantiated in the concepts of emotional signals, brain-computer interfaces, and generative artificial intelligence are explored. The generation of heuristics for identifying indicators of emotional signals is broken down into the prompt design activities and the execution of the prompt in the generative artificial intelligence tool. Data triangulation is employed to validate and enhance the reliability of the heuristic by comparing it with theoretical references and the researcher's position. As a central scope, this article constructs a set of heuristics through generative artificial intelligence to identify indicators of emotional signals, which are subsequently translated into inputs for the implementation of technological solutions for various sectors of society.