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Enrique Tomás Martínez Beltrán

Ph.D. student at the University of Murcia working at the intersection of federated learning, cybersecurity, and privacy-preserving AI for real-world systems.

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  3. Breaching Subjects’ Thoughts Privacy: A Study with Visual Stimuli and Brain-Computer Interfaces
Preprint2021

Journal of Healthcare Engineering

Breaching Subjects’ Thoughts Privacy: A Study with Visual Stimuli and Brain-Computer Interfaces

Brain-computer interfaces (BCIs) started being used in clinical scenarios, reaching nowadays new fields such as entertainment or learning. Using BCIs, neuronal activity can be monitored for various purposes, with the stu...

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Quick facts

Year
2021
Venue
Journal of Healthcare Engineering
Identifier
quilesperez2021breaching

Suggested citation

Mario Quiles Pérez, Enrique Tomás Martínez Beltrán, Sergio López Bernal, Alberto Huertas Celdrán, Gregorio Martínez Pérez (2021). Breaching Subjects’ Thoughts Privacy: A Study with Visual Stimuli and Brain-Computer Interfaces. Journal of Healthcare Engineering.

Abstract

Brain-computer interfaces (BCIs) started being used in clinical scenarios, reaching nowadays new fields such as entertainment or learning. Using BCIs, neuronal activity can be monitored for various purposes, with the study of the central nervous system response to certain stimuli being one of them, being the case of evoked potentials. However, due to the sensitivity of these data, the transmissions must be protected, with blockchain being an interesting approach to ensure the integrity of the data. This work focuses on the visual sense, and its relationship with the P300 evoked potential, where several open challenges related to the privacy of subjects’ information and thoughts appear when using BCI. The first and most important challenge is whether it would be possible to extract sensitive information from evoked potentials. This aspect becomes even more challenging and dangerous if the stimuli are generated when the subject is not aware or conscious that they have occurred. There is an important gap in this regard in the literature, with only one work existing dealing with subliminal stimuli and BCI and having an unclear methodology and experiment setup. As a contribution of this paper, a series of experiments, five in total, have been created to study the impact of visual stimuli on the brain tangibly. These experiments have been applied to a heterogeneous group of ten subjects. The experiments show familiar visual stimuli and gradually reduce the sampling time of known images, from supraliminal to subliminal. The study showed that supraliminal visual stimuli produced P300 potentials about 50% of the time on average across all subjects. Reducing the sample time between images degraded the attack, while the impact of subliminal stimuli was not confirmed. Additionally, younger subjects generally presented a shorter response latency. This work corroborates that subjects’ sensitive data can be extracted using visual stimuli and P300.

Authors

Mario Quiles PérezEnrique Tomás Martínez BeltránSergio López BernalAlberto Huertas CeldránGregorio Martínez Pérez

Keywords

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Preprint2026

Submitted to Information Fusion

Decentralized Self-Supervised Representation Learning via Prototype Exchange under Non-IID Data

Enrique Tomás Martínez Beltrán, Gérôme Bovet, Gregorio Martínez Pérez, Alberto Huertas Celdrán

Preprint2026

Submitted to Future Generation Computer Systems

FedEnD: Communication-Efficient Federated Learning for Non-IID Data via Decentralized Ensemble Distillation

Enrique Tomás Martínez Beltrán, Philip Giryes, Gérôme Bovet, Burkhard Stiller, Gregorio Martínez Pérez, Alberto Huertas Celdrán

Preprint2024

Submitted to International Journal of Information Security

Reputation System based on Distributed Ledger to Secure Decentralized Federated Learning

Jan von der Assen, Sandrin Raphael Hunkeler, Alberto Huertas Celdrán, Enrique Tomás Martínez Beltrán, Gérôme Bovet, Burkhard Stiller

Machine Learning (ML) faces several challenges, including susceptibility to data leakage and the overhead associated with data storage. Decentralized Federated Learning (DFL) offers a robust solution to these issues by e...

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