PERSPECTIVE article
Front. Comput. Sci.
Sec. Human-Media Interaction
Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1593905
This article is part of the Research TopicEmbodied Perspectives on Sound and Music AIView all 9 articles
Cyborg Synchrony: Integrating Human Physiology into Affective Generative Music AI
Provisionally accepted
- 1New York University, New York City, New York, United States
- 2University of California, Los Angeles, Los Angeles, United States
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As artificial intelligence (AI) systems become increasingly integrated into human social environments, their ability to foster meaningful interaction remains an open challenge. This paper shares personal insights into physical synchrony and social bonding from experiences with our musical neurofeedback prototypes. Building on research showing that physiological synchrony relates to social bonding, we speculate about an interpersonal musical biofeedback system that promotes synchrony by allowing users to attune to each other's physiological rhythms. This involves a two-stage AI training framework: (1) a Foundational Model trained on diverse listeners' physiological responses to a music library, and (2) Individualized Tuning, where the system fine-tunes itself to each user's unique physiological responses. By analyzing musical features alongside real-time physiological responses, the system dynamically generates personalized music, enabling new forms of embodied, nonverbal communication. This approach has the potential to actively enhance physiological synchrony, possibly fostering deeper social connection and emotional bonding.
Keywords: artificial intelligence, biofeedback, EEG, synchrony, Biosensors
Received: 14 Mar 2025; Accepted: 18 Aug 2025.
Copyright: © 2025 Ng, Sargent and Snell. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Senaida Ng, New York University, New York City, 10012, New York, United States
Kaia Sargent, University of California, Los Angeles, Los Angeles, United States
Jason Joel Snell, New York University, New York City, 10012, New York, United States
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