Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
This paper explores the novel approach of utilizing biofeedback signals, specifically electroencephalogram (EEG) and electrocardiogram (ECG) data, to optimize the performance of machine learning models. The core idea is to leverage the dynamic and often subtle information contained within these physiological signals as training data for machine learning algorithms. We propose a framework based on reinforcement learning, where the biofeedback signal directly influences the adjustment of model parameters. This allows for a feedback loop where the model adapts to the user's internal state, potentially leading to enhanced accuracy and personalized model performance. The presented methodology offers a fundamentally new paradigm for machine learning optimization, moving beyond traditional supervised learning approaches and opening possibilities for adaptive and responsive systems. The research highlights the potential for improved model training and a deeper understanding of the relationship between human physiology and machine learning outcomes.
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A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
