Remote Emotional Interactions via AI-Enhanced Brain-to-Body Neurophysiological Interface

Geovanna Evelyn Espinoza Taype, Maria Cecília Calani Baranauskas, Julio Cesar Dos Reis

2025

Abstract

The rapid growth of Artificial Intelligence (AI) has led to the emergence of Human-AI Interaction. This area explores how humans and AI systems can effectively collaborate and communicate. Recent studies have shown that using traditional approaches might not be adequate to capture issues arising from the combination of methods of these disciplines. A recent approach emerging in human-computer interaction (HCI), the so-cioenactive approach, represents a new possibility for capturing aspects in the confluence of AI and HCI due to its focus on the social-physical-digital coupling. In socioenactivity studies, the brain, body, senses, perception, cognition, sensorimotor, and emotions in interactions with people, physical objects, and computational systems. This study investigates and develops a socioenactive system empowered with AI that is designed to foster and enhance socio-emotional interactions between participants who are connected remotely. Our solution has the potential to significantly impact the field of Human-AI Interaction, by providing a deeper understanding of the interaction and coupling between human-AI through the socioenactive system. The so-cioenactive scenario involves a socioenactive system based on BCI (Brain Computer Interface) composed of several components: a mind wave device, smartwatch, parrot robot, and Aquarela Virtual system (which involves physical QR toys). These components are connected to share data remotely. The mind wave device and smartwatch collect neurophysiological information, and AI algorithms process this data to recognize emotions evoked by a parrot robot and the Aquarela Virtual. The AI component uses a machine learning technique to recognize emotions in brain waves (EEG) data. Our solution explores tree algorithms to recognize emotions in heart rate (ECG) data. Our evaluation, conducted in a workshop with participants from different nationalities and ages, demonstrates that the socioenactive system with embedded AI is a key driver of socio-emotional interactions. The system’s ability to interpret and utilize neurophysiological information to facilitate dynamic coupling between humans and technological processes might significantly advance Human-AI Interaction.

Paper Citation

in Harvard Style

Espinoza Taype G., Calani Baranauskas M. and Cesar Dos Reis J. (2025). Remote Emotional Interactions via AI-Enhanced Brain-to-Body Neurophysiological Interface. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-749-8, SciTePress, pages 533-543. DOI: 10.5220/0013280500003929

in Bibtex Style

@conference{iceis25,
author={Geovanna Espinoza Taype and Maria Calani Baranauskas and Julio Cesar Dos Reis},
title={Remote Emotional Interactions via AI-Enhanced Brain-to-Body Neurophysiological Interface},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2025},
pages={533-543},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013280500003929},
isbn={978-989-758-749-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - Remote Emotional Interactions via AI-Enhanced Brain-to-Body Neurophysiological Interface
SN - 978-989-758-749-8
AU - Espinoza Taype G.
AU - Calani Baranauskas M.
AU - Cesar Dos Reis J.
PY - 2025
SP - 533
EP - 543
DO - 10.5220/0013280500003929
PB - SciTePress