@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}

}

@inproceedings{cote_sexual_2024,

title = {Sexual Presence in Virtual Reality: A qEEG Exploration},

author = {S. S. -P. Côté and M. Brideau-Duquette and D. Labbé and P. Renaud},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195564684&doi=10.1109%2fVRW62533.2024.00270&partnerID=40&md5=3b0e533b4b5ab49d00103d5e71792d26},

doi = {10.1109/VRW62533.2024.00270},

isbn = {979-835037449-0 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW},

pages = {947–948},

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

abstract = {The increasing use of immersive technologies for sexual purposes raises questions about their capacity to enhance a unique aspect of presence-Sexual Presence (SP). Investigating this phenomenon hinges on our ability to measure it accurately. This paper improves our understanding of SP by identifying potential quantitative electroencephalography variables associated with SP. Twelve heterosexual cisgender males were exposed to virtual scenarios featuring sexual content performed by a Virtual Character (VC). After viewing, participants completed a Sexual Presence questionnaire. Correlations were observed between self-reported SP and the alpha band activity in the frontal and parietal regions. © 2024 IEEE.},

note = {Journal Abbreviation: Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW},

keywords = {Electroencephalography, Electrophysiology, Exposed to, Immersive technologies, Quantitative electroencephalography, Sexual Presence, Virtual character, virtual reality, Virtual scenario},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cote_combining_2021,

title = {Combining electroencephalography with plethysmography for classification of deviant sexual preferences.},

author = {S. S. -P. Cote and G. R. Paquette and S. -M. Neveu and S. Chartier and D. R. Labbe and P. Renaud},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113855965&doi=10.1109%2fIWBF50991.2021.9465078&partnerID=40&md5=b545b2a6d22e32115ac179399188960e},

doi = {10.1109/IWBF50991.2021.9465078},

isbn = {978-172819556-8 (ISBN)},

year  = {2021},

date = {2021-01-01},

booktitle = {Proceedings - 9th International Workshop on Biometrics and Forensics, IWBF 2021},

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

abstract = {Evaluating sexual preferences is a difficult task. Past researchrelied mostly on penile plethysmography (PPG). Even though this technique is the standard protocol used in most currentforensic settings, its usage showed mixed results. One way to improve PPG is the addition of other psychophysiological measures such as electroencephalography (EEG). However, EEG generates significant amount of data that hinders classification. Machine learning (ML) is nowadays an excellent tool to identify most discriminating variables and for classification. Therefore, it is proposed to use ML selection and extraction methods for dimensionality reduction and then to classify sexual preferences. Evidence from this proof of concept shows that using EEG and PPG together leads to better classification (85.6%) than using EEG (82.2%) or PPG individually (74.4%). The Random Forest (RF) classifier combined with the Principal Component Analysis (PCA) extraction method achieves a slightly higher general performance rate. This increase in performances opens the door for using more reliable biometric measures in the assessment of deviant sexual preferences. © 2021 IEEE.},

note = {Journal Abbreviation: Proc. - Int. Workshop Biom. Forensics, IWBF},

keywords = {Biometrics, Classification, Classification (of information), Decision trees, Deviant sexual preferences, Dimensionality reduction, Electroencephalography, Electrophysiology, extraction, Extraction method, Machine learning, Plethysmography, Proof of concept, Psychophysiological measures, Standard protocols, Variable selection and extraction},

pubstate = {published},

tppubtype = {inproceedings}

}