Economic Modeling with Brain-Computer Interface Controlled Data Systems
Keywords:
Economic Modeling, Brain-Computer Interface (BCI), Computational Economics, Neural Data Integration, Cognitive Computing, Data Analytics, Predictive Modeling, Human-Computer InteractionAbstract
Economic Modeling using BCI technology improves this study's forecasting accuracy and decision-making speed. The primary objectives are to evaluate how BCI cognitive data (EEG, fNIRS, MEG, fMRI, and ECoG) influences economic forecasts and address ethical and privacy concerns. Secondary data assesses prediction accuracy and decision-making speed before and after BCI data integration. Many BCI technologies improved model performance, including 18.9% prediction accuracy and 31.6% decision-making speed. These discoveries demonstrate how real-time BCI cognitive insights might enhance economic forecasting. However, data security, integration complexity, and high expenses must be addressed. The paper recommends solid legal frameworks, data integration standards, and BCI technological funding and assistance to address these obstacles. Data protection laws and ethical standards are needed to guarantee appropriate BCI usage. BCI technology has transformational potential for economic Modeling, but its limits must be considered, and sound policies must be adopted to maximize its advantages.
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