Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Santana, S. T. O. D.; Silva, J. C. Da; Hatano, Y.; Guerrero-Mendez, C. D.; Souza, M. L. De; Igasaki, T.; Bastos-Filho, T.; Dantas, A. F. O. De Azevedo; Falk, T. H.; Delisle-Rodriguez, D.
Cognitive Training Using a Brain-Computer Interface for θ/β Ratio Self-Regulation - Preliminary Results with Long Covid-19 Survivors Article d'actes
Dans: IEEE Int. Conf. Hum.-Mach. Syst., ICHMS, p. 646–650, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 979-833154511-6 (ISBN), (Journal Abbreviation: IEEE Int. Conf. Hum.-Mach. Syst., ICHMS).
Résumé | Liens | BibTeX | Étiquettes: 'current, Behavioral research, Brain, brain computer interface, Brain mapping, cognitive impairment, Cognitive skill, Cognitive systems, Cognitive training, Computer games, COVID-19, Electroencephalography, Electrophysiology, Executive Function, Interfaces (computer), Memory functions, Neurotechnology, Personnel training, Self regulation, Sustained attention, working memory
@inproceedings{santanaCognitiveTrainingUsing2026,
title = {Cognitive Training Using a Brain-Computer Interface for θ/β Ratio Self-Regulation - Preliminary Results with Long Covid-19 Survivors},
author = {S. T. O. D. Santana and J. C. Da Silva and Y. Hatano and C. D. Guerrero-Mendez and M. L. De Souza and T. Igasaki and T. Bastos-Filho and A. F. O. De Azevedo Dantas and T. H. Falk and D. Delisle-Rodriguez},
url = {https://www.scopus.com/pages/publications/105045602964?origin=resultslist},
doi = {10.1109/ICHMS69701.2026.11602366},
isbn = {979-833154511-6 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {IEEE Int. Conf. Hum.-Mach. Syst., ICHMS},
pages = {646–650},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Long COVID-19 can generate cognitive impairments, affecting a person's attention, working memory, and executive functions. Previous studies have suggested that therapies based on neurotechnologies can be used to recover cognitive skills. This current research presents an electroencephalography brain-computer interface based serious game for sustained attention training. The system classifies attention levels while the user controls the speed of a virtual car by self-regulating the θ/β power band ratio across multiple brain locations. Pilot experiments were conducted with two COVID-19 adult survivors who experienced cognitive decline due to long COVID19 symptoms. Participants underwent 10 sessions across consecutive working days (2 sessions × week). Both participants enhanced brain wave oscillations in both frontal and central regions, reducing theta activity and simultaneously increasing beta oscillations) after the training, achieving therefore healthy brain behavior during a cognitive attention task. Furthermore, both individuals improved cognitive skills, such as attention, working memory, and executive functions. These preliminary findings are promising and motivate the further development of multichannel neurofeedback systems for cognitive training for long COVID-19 survivors. © 2026 IEEE.},
note = {Journal Abbreviation: IEEE Int. Conf. Hum.-Mach. Syst., ICHMS},
keywords = {'current, Behavioral research, Brain, brain computer interface, Brain mapping, cognitive impairment, Cognitive skill, Cognitive systems, Cognitive training, Computer games, COVID-19, Electroencephalography, Electrophysiology, Executive Function, Interfaces (computer), Memory functions, Neurotechnology, Personnel training, Self regulation, Sustained attention, working memory},
pubstate = {published},
tppubtype = {inproceedings}
}
Long COVID-19 can generate cognitive impairments, affecting a person's attention, working memory, and executive functions. Previous studies have suggested that therapies based on neurotechnologies can be used to recover cognitive skills. This current research presents an electroencephalography brain-computer interface based serious game for sustained attention training. The system classifies attention levels while the user controls the speed of a virtual car by self-regulating the θ/β power band ratio across multiple brain locations. Pilot experiments were conducted with two COVID-19 adult survivors who experienced cognitive decline due to long COVID19 symptoms. Participants underwent 10 sessions across consecutive working days (2 sessions × week). Both participants enhanced brain wave oscillations in both frontal and central regions, reducing theta activity and simultaneously increasing beta oscillations) after the training, achieving therefore healthy brain behavior during a cognitive attention task. Furthermore, both individuals improved cognitive skills, such as attention, working memory, and executive functions. These preliminary findings are promising and motivate the further development of multichannel neurofeedback systems for cognitive training for long COVID-19 survivors. © 2026 IEEE.
2.
Moudoud, H.; Houda, Z. A. E.; Brik, B.
LLMs to Secure Consumer Networks: Open Problems and Future Directions Article de journal
Dans: IEEE Consumer Electronics Magazine, vol. 14, no 5, p. 51–59, 2025, ISSN: 21622248 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: 'current, Comprehensive research, Cyber threats, Defence mechanisms, Forward looking, Generative adversarial networks, Innovative solutions, Language model, Research problems, Security mechanism, Smart Home Technology
@article{moudoudLLMsSecureConsumer2025,
title = {LLMs to Secure Consumer Networks: Open Problems and Future Directions},
author = {H. Moudoud and Z. A. E. Houda and B. Brik},
url = {https://www.scopus.com/pages/publications/85217963109?origin=resultslist},
doi = {10.1109/MCE.2025.3542247},
issn = {21622248 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Consumer Electronics Magazine},
volume = {14},
number = {5},
pages = {51–59},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The increasing complexity of consumer networks, characterized by the rapid adoption of Internet of Things devices and smart home technologies, exposes significant limitations in traditional security mechanisms. These challenges drive a growing interest in innovative solutions, such as generative artificial intelligence, including large language models (LLMs) to ensure the security of consumer networks from evolving cyber threats. In this article, we present a forward-looking perspective on the role of LLMs in securing consumer networks. Then, we present a comprehensive study of attacks targeting LLMs and current defense mechanisms/strategies. Finally, we present a set of core research problems and a comprehensive research agenda that identifies future directions to advance LLM capabilities for consumer network security. © 2012 IEEE.},
keywords = {'current, Comprehensive research, Cyber threats, Defence mechanisms, Forward looking, Generative adversarial networks, Innovative solutions, Language model, Research problems, Security mechanism, Smart Home Technology},
pubstate = {published},
tppubtype = {article}
}
The increasing complexity of consumer networks, characterized by the rapid adoption of Internet of Things devices and smart home technologies, exposes significant limitations in traditional security mechanisms. These challenges drive a growing interest in innovative solutions, such as generative artificial intelligence, including large language models (LLMs) to ensure the security of consumer networks from evolving cyber threats. In this article, we present a forward-looking perspective on the role of LLMs in securing consumer networks. Then, we present a comprehensive study of attacks targeting LLMs and current defense mechanisms/strategies. Finally, we present a set of core research problems and a comprehensive research agenda that identifies future directions to advance LLM capabilities for consumer network security. © 2012 IEEE.