Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Bogie, B. J. M.; Noël, C.; Gu, F.; Nadeau, S.; Shvetz, C.; Khan, H.; Rivard, M. -C.; Bouchard, S.; Lepage, M.; Guimond, S.
Using virtual reality to improve verbal episodic memory in schizophrenia: A proof-of-concept trial Article de journal
Dans: Schizophrenia Research: Cognition, vol. 36, 2024, ISSN: 22150013 (ISSN), (Publisher: Elsevier Inc.).
Résumé | Liens | BibTeX | Étiquettes: adult, article, clinical article, clinical assessment, Cognitive remediation therapy, cybersickness, disease severity, dizziness, Ecological treatment, Episodic memory, exclusion VR criteria questionnaire, feasibility study, female, Hopkins verbal learning test, human, male, mini international neuropsychiatric interview, nausea, outcome assessment, Positive and Negative Syndrome Scale, Proof of concept, questionnaire, randomized controlled trial, schizophrenia, scoring system, Semantic encoding, Semantics, task performance, training, Verbal memory, virtual reality, vr experience questionnaire
@article{bogie_using_2024,
title = {Using virtual reality to improve verbal episodic memory in schizophrenia: A proof-of-concept trial},
author = {B. J. M. Bogie and C. Noël and F. Gu and S. Nadeau and C. Shvetz and H. Khan and M. -C. Rivard and S. Bouchard and M. Lepage and S. Guimond},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186986986&doi=10.1016%2fj.scog.2024.100305&partnerID=40&md5=a15c598b45b8f44a40b25fe5fd078a06},
doi = {10.1016/j.scog.2024.100305},
issn = {22150013 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Schizophrenia Research: Cognition},
volume = {36},
abstract = {Background: Schizophrenia is associated with impairments in verbal episodic memory. Strategy for Semantic Association Memory (SESAME) training represents a promising cognitive remediation program to improve verbal episodic memory. Virtual reality (VR) may be a novel tool to increase the ecological validity and transfer of learned skills of traditional cognitive remediation programs. The present proof-of-concept study aimed to assess the feasibility, acceptability, and preliminary efficacy of a VR-based cognitive remediation module inspired by SESAME principles to improve the use of verbal episodic memory strategies in schizophrenia. Methods: Thirty individuals with schizophrenia/schizoaffective disorder completed this study. Participants were randomized to either a VR-based verbal episodic memory training condition inspired by SESAME principles (intervention group) or an active control condition (control group). In the training condition, a coach taught semantic encoding strategies (active rehearsal and semantic clustering) to help participants remember restaurant orders in VR. In the active control condition, participants completed visuospatial puzzles in VR. Attrition rate, participant experience ratings, and cybersickness questionnaires were used to assess feasibility and acceptability. Trial 1 of the Hopkins Verbal Learning Test – Revised was administered pre- and post-intervention to assess preliminary efficacy. Results: Feasibility was demonstrated by a low attrition rate (5.88 %), and acceptability was demonstrated by limited cybersickness and high levels of enjoyment. Although the increase in the number of semantic clusters used following the module did not reach conventional levels of statistical significance in the intervention group, it demonstrated a notable trend with a medium effect size (t = 1.48},
note = {Publisher: Elsevier Inc.},
keywords = {adult, article, clinical article, clinical assessment, Cognitive remediation therapy, cybersickness, disease severity, dizziness, Ecological treatment, Episodic memory, exclusion VR criteria questionnaire, feasibility study, female, Hopkins verbal learning test, human, male, mini international neuropsychiatric interview, nausea, outcome assessment, Positive and Negative Syndrome Scale, Proof of concept, questionnaire, randomized controlled trial, schizophrenia, scoring system, Semantic encoding, Semantics, task performance, training, Verbal memory, virtual reality, vr experience questionnaire},
pubstate = {published},
tppubtype = {article}
}
Background: Schizophrenia is associated with impairments in verbal episodic memory. Strategy for Semantic Association Memory (SESAME) training represents a promising cognitive remediation program to improve verbal episodic memory. Virtual reality (VR) may be a novel tool to increase the ecological validity and transfer of learned skills of traditional cognitive remediation programs. The present proof-of-concept study aimed to assess the feasibility, acceptability, and preliminary efficacy of a VR-based cognitive remediation module inspired by SESAME principles to improve the use of verbal episodic memory strategies in schizophrenia. Methods: Thirty individuals with schizophrenia/schizoaffective disorder completed this study. Participants were randomized to either a VR-based verbal episodic memory training condition inspired by SESAME principles (intervention group) or an active control condition (control group). In the training condition, a coach taught semantic encoding strategies (active rehearsal and semantic clustering) to help participants remember restaurant orders in VR. In the active control condition, participants completed visuospatial puzzles in VR. Attrition rate, participant experience ratings, and cybersickness questionnaires were used to assess feasibility and acceptability. Trial 1 of the Hopkins Verbal Learning Test – Revised was administered pre- and post-intervention to assess preliminary efficacy. Results: Feasibility was demonstrated by a low attrition rate (5.88 %), and acceptability was demonstrated by limited cybersickness and high levels of enjoyment. Although the increase in the number of semantic clusters used following the module did not reach conventional levels of statistical significance in the intervention group, it demonstrated a notable trend with a medium effect size (t = 1.48
2.
Cote, S. S. -P.; Paquette, G. R.; Neveu, S. -M.; Chartier, S.; Labbe, D. R.; Renaud, P.
Combining electroencephalography with plethysmography for classification of deviant sexual preferences. Article d'actes
Dans: Proceedings - 9th International Workshop on Biometrics and Forensics, IWBF 2021, Institute of Electrical and Electronics Engineers Inc., 2021, ISBN: 978-172819556-8 (ISBN), (Journal Abbreviation: Proc. - Int. Workshop Biom. Forensics, IWBF).
Résumé | Liens | BibTeX | Étiquettes: 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
@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}
}
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.