@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{maldonado-bouchardNeuralEndophenotypesSecure2026,

title = {From Neural Endophenotypes to Secure Human-Machine Interaction: Identifying Empathy-Related Vulnerabilities from Resting-State qEEG using Deep Learning},

author = {S. Maldonado-Bouchard and A. Saki and U. Faghihi and M. Brideau-Duquette and P. Renaud},

url = {https://www.scopus.com/pages/publications/105042126330?origin=resultslist},

doi = {10.1109/CAI68641.2026.11536416},

isbn = {979-833156039-3 (ISBN)},

year  = {2026},

date = {2026-01-01},

booktitle = {IEEE Conf. Artif. Intell., CAI},

pages = {1725–1732},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Telepresence and extended reality (XR) platforms increasingly integrate physiological sensing to infer socio-affective traits and personalize interaction. We test whether resting-state electroencephalography (EEG) alone can predict inter-individual differences in callous affect (SRP-CA; a proxy for reduced affective empathy) in a forensic cohort. Using 32-channel qEEG recorded at 500 Hz during one minute of eyes-open (EO) followed by one minute of eyes-closed (EC), we retain 143 quality-controlled sessions from 72 participants, each paired with a session-level SRP-CA score. We implement an EEG-only, leakage-aware evaluation protocol: splitting is performed at the session level and normalization parameters are fit on training data only. EEG is segmented into overlapping windows of 250-1,000 samples (50% overlap), and a lightweight Transformer encoder regressor is trained per window length; session-level predictions are obtained by averaging window-level outputs. On held-out test sessions, the best performance is achieved with 400-sample windows (MSE = 0.0595},

note = {Journal Abbreviation: IEEE Conf. Artif. Intell., CAI},

keywords = {Deep learning, Electroencephalography, Electrophysiology, Evaluation protocol, Human machine interaction, Inter-individual differences, Matchings, Physiological sensing, Resting state, Sample windows, Session level, Splittings, telepresence, Template matching, Visual communication},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{tiwariQuantifyingBiologicalSex2026,

title = {Quantifying Biological Sex Leakage in Electroencephalography-Based Mental Workload Measurement and Its Impact on Model Performance},

author = {A. Tiwari and R. Arrabito and A. Davoust and T. H. Falk},

url = {https://www.scopus.com/pages/publications/105045575255?origin=resultslist},

doi = {10.1109/ICHMS69701.2026.11602273},

isbn = {979-833154511-6 (ISBN)},

year  = {2026},

date = {2026-01-01},

booktitle = {IEEE Int. Conf. Hum.-Mach. Syst., ICHMS},

pages = {392–397},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Detection of mental workload is essential for designing systems that can monitor and adapt to the needs of human operators, thereby ensuring their optimal performance and well-being. Features derived from neurophysiological signals, particularly electroencephalograms (EEG), provide a realtime, unobtrusive, and objective indicator of mental workload. However, EEG signals are often correlated with demographic variables, including biological sex and age, which can introduce systematic performance differences across demographic groups, while unintentionally encoding demographic information into models trained on EEG data. In this work, we investigate and quantify the influence of user demographics on mental workload detection models using fairness analysis. Furthermore, we propose domain-adversarial training, in which an adversary penalizes the model for retaining sex-related information, to mitigate leakage of private demographic information (biological sex). The proposed approach simultaneously increases the odds ratio for sex from 0.83 to 0.90 (values closer to 1 indicate reduced demographic disparity) while reducing statistical significance. This approach, however, also leads to a reduction in mental workload detection performance, with a 12.7% decrease in balanced accuracy (BACC), suggesting an inherent trade-off between performance and privacy protection. © 2026 IEEE.},

note = {Journal Abbreviation: IEEE Int. Conf. Hum.-Mach. Syst., ICHMS},

keywords = {Biomedical signal processing, Demographic information, Designing systems, Economic and social effects, Electroencephalography, Electrophysiology, Human operator, Mental workload, Modeling performance, Optimal performance, Performance, Population statistics, Real- time, Well being, Workload measurements},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lopesSubjectiveObjectiveEvaluation2025,

title = {Subjective and Objective Evaluation of the Benefits of Multisensory Virtual Nature Immersion for Patients with Post-Traumatic Stress Disorder},

author = {M. K. S. Lopes and L. Perreault and B. J. De Jesus and M. -C. Roberge and T. H. Falk},

url = {https://www.scopus.com/pages/publications/105023830754?origin=resultslist},

doi = {10.1109/QoMEX65720.2025.11219945},

isbn = {979-833155435-4 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Int. Conf. Qual. Multimed. Exp., QoMEX},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Multisensory virtual nature immersion has been shown to assist with mental health management. In this paper, we look at the benefits of two multisensory immersion modalities for patients diagnosed with post-traumatic stress disorder (PTSD). The modalities include: (1) a portable solution that integrates scent diffusion within the head-mounted display (HMD), and (2) a pod-based solution, in which a multisensory pod provides olfactory and haptic feedback (i.e., wind and vibrations) alongside audio-visual information via the HMD. We assess the two methods subjectively, via numerous questionnaires, and objectively, via measures computed from electroencephalography signals. The pod-based intervention resulted in greater improvements in PTSD symptoms and in sustained attention, with improvements in several objective measures of user experience, including engagement, valence, arousal, and sense of immersion/presence. © 2025 IEEE.},

note = {Journal Abbreviation: Int. Conf. Qual. Multimed. Exp., QoMEX},

keywords = {Audio-visual information, EEG, Electroencephalography, Electronic nose, Electrophysiology, Haptic feedbacks, Head-mounted-displays, Health management, Human computer interaction, Mental health, Multisensory, Multisensory nature immersion, Occupational diseases, Patient monitoring, posttraumatic stress disorder, PTSD, Subjective and objective evaluations, User experience, User interfaces, Users' experiences, virtual reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jaberiQuantifyingRiskPrivate2025,

title = {Quantifying the Risk of Private Information Leakage in the Metaverse with EEG-Instrumented Virtual Reality Headsets},

author = {M. Jaberi and S. Bouchard and T. H. Falk},

url = {https://www.scopus.com/pages/publications/105033158377?origin=resultslist},

doi = {10.1109/SMC58881.2025.11343511},

isbn = {1062922X (ISSN); 979-833153358-8 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},

pages = {4293–4298},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {As virtual reality (VR) technologies become more immersive and metaverse applications burgeon, modern headsets are increasingly becoming equipped with sensors capable of capturing multiple neurophysiological signals. While these signals can be used to measure, in real-time, quality of experience metrics that can be used to enhance interactivity and user engagement, they may also introduce novel privacy risks by unintentionally leaking sensitive personal attributes. In this paper, we explore the extent in which electroencephalography (EEG) signals, recorded during an immersive VR memory task, can be used to infer users' private information, such as age, biological sex, and identity. We employ both classical machine learning models with hand-crafted features, as well as end-to-end deep learning approaches. Our findings demonstrate that EEG-based features can, indeed, leak information about biological sex, age, and user identity, with end-to-end models obtaining the best performance. Feature importance ranking and deep neural network saliency maps were used to provide explainability on the neural patterns used by the models. We conclude with recommendations on how these findings can also be used to help secure future metaverse applications. © 2025 IEEE.},

note = {Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},

keywords = {Biomedical signal processing, Electroencephalography, Electrophysiology, Immersive, Information leakage, Interactivity, Learning systems, Metaverses, Neurophysiology, Privacy risks, Private information, Real-time quality, User engagement, virtual reality, Virtual reality technology, Virtual-reality headsets},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{dejesusElectroencephalographyNeuromarkersPredict2025,

title = {Electroencephalography Neuromarkers to Predict the Response of a Multisensory Virtual Reality Nature Immersion Intervention for Patients Diagnosed with Post-Traumatic Stress Disorder},

author = {B. De Jesus and M. Lopes and L. Perreault and M. -C. Roberge and A. A. De Oliveira and T. H. Falk},

url = {https://www.scopus.com/pages/publications/105033154892?origin=resultslist},

doi = {10.1109/SMC58881.2025.11342787},

isbn = {1062922X (ISSN); 979-833153358-8 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},

pages = {6033–6038},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Immersive virtual reality (VR) applications rapidly expand across domains, including training, gaming, and healthcare. More recently, multisensory immersive experiences, including olfactory and haptic stimulation, have emerged and shown great promise, especially for interventions in well-being and mental health management. Multisensory experiences, however, are very subjective (e.g., one subject may like certain smells, while others do not), and recent results have suggested that some participants may not respond positively to the treatment. As multisensory VR interventions can be costly and time-consuming for both patients and clinicians, being able to find neuromarkers that predict intervention outcomes would be invaluable. Here, we aim to take the first steps in the development of a neuromarker to predict the response to a multisensory nature immersion VR intervention. A pilot experiment was performed with twenty patients diagnosed with post-traumatic stress disorder. Potential neuromarkers are extracted from electroencephalography (EEG) signals measured from an instrumented VR headset. We show that some EEG patterns start to differ between responders and non-responders as early as the fourth session, i.e., one-third of the way into the entire intervention. This suggests that neuromarkers to predict the outcomes of a multisensory VR immersion intervention may exist. These markers could be used not only to save time and resources for clinicians and patients but also to promote precision treatment where interventions are adjusted to each patient, maximizing success rates. © 2025 IEEE.},

note = {Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},

keywords = {Biomedical signal processing, Diseases, Electroencephalography, electroencephalography (EEG), Electrophysiology, Haptics, Immersive, Immersive virtual reality, Multisensory, Nature immersion, Odors, Olfactory, Patient monitoring, Patient treatment, Post-traumatic stress disorder, Post-traumatic stress disorder (PTSD), posttraumatic stress disorder, virtual reality, virtual reality (VR)},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cote_investigating_2024,

title = {Investigating Female Sexual Presence Through Triangulation of Behavioral and Physiological Measures in Virtual Reality: Towards Therapeutic Applications for Sexual Disorders},

author = {S. S. -P. Côté and M. Brideau-Duquette and D. Lafortune and J. G. Pfaus and P. Renaud},

editor = {Poquet O. and Ortega-Arranz A. and Viberg O. and Chounta I.-A. and McLaren B. and Jovanovic J.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193914916&doi=10.5220%2f0012754700003693&partnerID=40&md5=435ba537cddf1277ed1b459b8a0b1984},

doi = {10.5220/0012754700003693},

isbn = {21845026 (ISSN); 978-989758697-2 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {International Conference on Computer Supported Education, CSEDU - Proceedings},

volume = {1},

pages = {694–700},

publisher = {Science and Technology Publications, Lda},

abstract = {Exposure to sexual contexts by means of immersive, extended reality technologies, offer an opportunity to both: better understand sexual responding, and in turn, offers insights as to how the same technology could help in treating sexual disorders. The present papers reports on the ability of behavioural (i.e., oculometry) and physiological (i.e., electroencephalography and vaginal plethysmography) to conjointly predict subjective sexual feelings (i.e., subjective sexual presence), this, using a sample of 12 heterosexual cisgendered women. Measurements pertained to the participants living a sexual immersion (via a virtual reality headset) with an opposite sex virtual character engaging in sexually suggestive behaviour. Results suggest that all the tested behavioural and physiological measurements could play a role in the shaping of sexual presence. Results are discussed with therapeutic learning processes considerations in mind. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.},

note = {Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings},

keywords = {Affordances, Behavioral measures, Computer vision, E-learning, Electroencephalography, Electrophysiology, Gaze Behaviour, Gaze behaviours, Immersive, Learning, Physiological measures, Quantitative electroencephalography, Quantitative Electroencephalography (qEEG), Sexual Presence, Therapeutic Application, Vaginal Photoplethysmography, virtual reality},

pubstate = {published},

tppubtype = {inproceedings}

}