AI tools for analysis of empirical experiments on economic behavior
AI tools for analysis of empirical experiments on economic behavior

Summary/Abstract: This paper introduces a methodological framework designed to democratize neuroeconomics by integrating 4-channel EEG hardware (Muse 2) with advanced computational workflows. The central thesis argues for a transition from static, linear feature extraction to dynamic, spatio-temporal modeling to capture the evolving cognitive processes of economic decision-making. By leveraging the "NeuroSense" pipeline—utilizing the meegkit library for robust ringing artifact reduction—researchers can maintain high signal integrity from portable, dry-electrode devices.The core of the framework utilizes the "NeuCube" Spiking Neural Network (SNN) architecture, which employs biologically plausible learning rules like Spike-Timing-Dependent Plasticity (STDP). This architecture effectively upgrades sparse surface data into a 3D evolving brain model, allowing for the reconstruction of complex neural states. The utility of this unified pipeline is demonstrated through three canonical economic paradigms: Willingness to Pay (WTP), the Ultimatum Game (UG), and Frontal Alpha Asymmetry (FAA). This approach enhances the ecological validity of neuroeconomic research, enabling rigorous "in-the-wild" experiments in real-world economics.

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