@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{boukhalfi_tools_2015,

title = {Tools and techniques for real-time data acquisition and analysis in brain computer interface studies using qEEG and eye tracking in virtual reality environment},

author = {T. Boukhalfi and C. Joyal and S. Bouchard and S. M. Neveu and P. Renaud},

editor = {Zaremba M. Sasiadek J. Dolgui A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953850845&doi=10.1016%2fj.ifacol.2015.06.056&partnerID=40&md5=5eaa3cab6df7946f02c74e4aded68eac},

doi = {10.1016/j.ifacol.2015.06.056},

issn = {24058963},

year  = {2015},

date = {2015-01-01},

booktitle = {IFAC-PapersOnLine},

volume = {28},

number = {3},

pages = {46–51},

abstract = {In this paper, we present the pipeline of data acquisition and analysis used in the ARViPL lab at the Montreal Philippe-Pinel Institute for different studies related to forensic psychiatry in Virtual Reality (VR) environment and we discuss the different challenges that we encounter during the experiments when combining different new technologies that help researchers to better understand the underlying mechanisms of various mental health disorders. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},

note = {ISSN: 24058963 

Issue: 3},

keywords = {Acquisition, Analysis, brain computer interface, Data acquisition, Electroencephalography, eye tracking, Eye-tracking, Forensic, Forensic engineering, Heat maps, Interfaces (computer), Psychiatry, Real time, virtual reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{boukhalfiToolsTechniquesRealtime2015,

title = {Tools and techniques for real-time data acquisition and analysis in brain computer interface studies using qEEG and eye tracking in virtual reality environment},

author = {T. Boukhalfi and C. Joyal and S. Bouchard and S. M. Neveu and P. Renaud},

editor = {Zaremba M. Sasiadek J. Dolgui A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953850845&doi=10.1016%2fj.ifacol.2015.06.056&partnerID=40&md5=5eaa3cab6df7946f02c74e4aded68eac},

doi = {10.1016/j.ifacol.2015.06.056},

issn = {24058963},

year  = {2015},

date = {2015-01-01},

booktitle = {IFAC-PapersOnLine},

volume = {28},

number = {3},

pages = {46–51},

abstract = {In this paper, we present the pipeline of data acquisition and analysis used in the ARViPL lab at the Montreal Philippe-Pinel Institute for different studies related to forensic psychiatry in Virtual Reality (VR) environment and we discuss the different challenges that we encounter during the experiments when combining different new technologies that help researchers to better understand the underlying mechanisms of various mental health disorders. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},

keywords = {Acquisition, Analysis, brain computer interface, Data acquisition, Electroencephalography, eye tracking, Eye-tracking, Forensic, Forensic engineering, Heat maps, Interfaces (computer), Psychiatry, Real time, virtual reality},

pubstate = {published},

tppubtype = {inproceedings}

}