Artículo de revista2025

Neurocomputing

When Brain–Computer Interfaces meet the metaverse: Landscape, demonstrator, trends, challenges, and concerns

The metaverse has gained tremendous popularity in recent years, allowing the interconnection of users worldwide. However, current systems in metaverse scenarios, such as virtual reality glasses, offer a partial immers...

MetaverseBrain–Computer InterfacesTrendsChallengesConcerns

Resumen

The metaverse has gained tremendous popularity in recent years, allowing the interconnection of users worldwide. However, current systems in metaverse scenarios, such as virtual reality glasses, offer a partial immersive experience. In this context, Brain–Computer Interfaces (BCIs) can introduce a revolution in the metaverse, although a study of the applicability and implications of BCIs in these virtual scenarios is required. Based on a limited number of publications, this work reviews the applicability of BCIs in the metaverse, analyzing the current status of this integration based on different categories related to virtual worlds and the evolution of BCIs in these scenarios in the medium and long term. This work also proposes the design and implementation of a general framework that integrates BCIs with different data sources from sensors and actuators (e.g., VR glasses) based on a modular design to be easily extended. This manuscript also validates the framework in a demonstrator consisting of driving a car within a metaverse, using a BCI for neural data acquisition, a VR headset to provide realism, and a steering wheel and pedals. Four use cases (UCs) are selected, focusing on cognitive and emotional assessment of the driver, detection of drowsiness, and driver authentication while using the vehicle. The results demonstrate the applicability of BCIs to metaverse scenarios using the proposed framework, achieving over 80% F1-score for all UCs, with performance close to 100% for detecting emotions and authenticating users. Moreover, this manuscript offers an analysis of BCI trends in the metaverse, also identifying future challenges that the intersection of these technologies will face. Finally, it reviews the concerns that using BCIs in virtual world applications could generate according to different categories: accessibility, user inclusion, privacy, cybersecurity, physical safety, and ethics.

Autores

Sergio López BernalMario Quiles PérezEnrique Tomás Martínez BeltránGregorio Martínez PérezAlberto Huertas Celdrán

Palabras clave

MetaverseBrain–Computer InterfacesTrendsChallengesConcerns

Publicaciones relacionadas

Publicaciones relacionadas por tema, método o aplicación.

Artículo de revista2024

Neurocomputing

NeuronLab: BCI framework for the study of biosignals

Sergio López Bernal, Juan Antonio Martínez López, Enrique Tomás Martínez Beltrán, Mario Quiles Pérez, Gregorio Martínez Pérez, Alberto Huertas Celdrán

Brain–Computer Interfaces (BCIs) allow the acquisition of brain activity using non-invasive techniques such as Electroencephalography (EEG). Since BCI devices do not commonly interpret the acquired EEG signals, extern...

Artículo de revista2022

Expert Systems with Applications

SAFECAR: A Brain–Computer Interface and intelligent framework to detect drivers’ distractions

Enrique Tomás Martínez Beltrán, Mario Quiles Pérez, Sergio López Bernal, Gregorio Martínez Pérez, Alberto Huertas Celdrán

As recently reported by the World Health Organization (WHO), the high use of intelligent devices such as smartphones, multimedia systems, or billboards causes an increase in distraction and, consequently, fatal accide...

Artículo de revista2025

IEEE Communications Magazine

Flighter: Decentralized Federated Learning and Situational Awareness for Secure Military Aerial Reconnaissance

Enrique Tomás Martínez Beltrán, Pedro Miguel Sánchez Sánchez, Gérôme Bovet, Burkhard Stiller, Gregorio Martínez Pérez, Alberto Huertas Celdrán

Mosaic warfare is a military strategy where reconnaissance missions with aerial vehicles are critical for gathering enemy information and achieving battlefield dominance. Nowadays, machine learning (ML) techniques pla...

Investigación relacionada