@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}

}

@inproceedings{davoust_user_2016,

title = {User participation and honesty in online rating systems: What a social network can do},

author = {A. Davoust and B. Esfandiari},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021919921&partnerID=40&md5=6a33a1ab6d3b6ddd037240f4f664b6fe},

isbn = {978-1-57735-759-9},

year  = {2016},

date = {2016-01-01},

booktitle = {AAAI Workshop - Technical Report},

volume = {WS-16-01 - WS-16-15},

pages = {477–483},

publisher = {AI Access Foundation},

abstract = {An important problem with online communities in general, and online rating systems in particular, is uncooperative behavior: lack of user participation, dishonest contributions. This may be due to an incentive structure akin to a Prisoners' Dilemma (PD). We show that introducing an explicit social network to PD games fosters cooperative behavior, and use this insight to design a new aggregation technique for online rating systems. Using a dataset of ratings from Yelp, we show that our aggregation technique outperforms Yelp's proprietary filter, as well as baseline techniques from recommender systems. Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org).},

keywords = {Aggregation techniques, Artificial intelligence, Behavioral research, Big data, Co-operative behaviors, Cognitive systems, Computer games, Computer programming, Computer systems programming, Data mining, Hybrid systems, Incentive structure, On-line communities, Online rating systems, Online systems, Population statistics, Prisoners' Dilemma, Rating, Social networking (online), User participation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{davoustUserParticipationHonesty2016,

title = {User participation and honesty in online rating systems: What a social network can do},

author = {A. Davoust and B. Esfandiari},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021919921&partnerID=40&md5=6a33a1ab6d3b6ddd037240f4f664b6fe},

isbn = {978-1-57735-759-9},

year  = {2016},

date = {2016-01-01},

booktitle = {AAAI Workshop - Technical Report},

volume = {WS-16-01 - WS-16-15},

pages = {477–483},

publisher = {AI Access Foundation},

abstract = {An important problem with online communities in general, and online rating systems in particular, is uncooperative behavior: lack of user participation, dishonest contributions. This may be due to an incentive structure akin to a Prisoners' Dilemma (PD). We show that introducing an explicit social network to PD games fosters cooperative behavior, and use this insight to design a new aggregation technique for online rating systems. Using a dataset of ratings from Yelp, we show that our aggregation technique outperforms Yelp's proprietary filter, as well as baseline techniques from recommender systems. Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org).},

keywords = {Aggregation techniques, Artificial intelligence, Behavioral research, Big data, Co-operative behaviors, Cognitive systems, Computer games, Computer programming, Computer systems programming, Data mining, Hybrid systems, Incentive structure, On-line communities, Online rating systems, Online systems, Population statistics, Prisoners' Dilemma, Rating, Social networking (online), User participation},

pubstate = {published},

tppubtype = {inproceedings}

}