Proposal for a tool for the applicability of VR and BCI in an interdisciplinary study to infer attention in individuals with ADHD
Resumo
Attention Deficit Hyperactivity Disorder (ADHD) is a persistent pattern of inattention and/or hyperactivity-impulsivity and has behavioral and pharmacological treatments as alternatives. For action in behavioral treatment, there are several alternatives based on psychological techniques. The present study presents the proposal of evaluating the attention rate of an individual with ADHD by using virtual reality equipment, brainwave analysis, and eye movement tracking. The proposal emerges as a way of integrating psychometric methods aligned with current technologies that can offer data to measure parameters that determine attention. The presented method uses virtual reality as a stimulus scenario, controlled by the researchers and professionals involved, and subsequently performs the reading of brainwaves to capture data. The research’s innovation is the integration of technologies that have recently become available for mass use with psychometric methods, especially for people with ADHD, and it explores how health professionals can use data obtained from technological artifacts. The present work is a collaboration of a multidisciplinary team formed by computing and pedagogy researchers and a psychology professional.
Referências
Anika Alim and Masudul H Imtiaz. 2023. Automatic identification of children with adhd from eeg brain waves. Signals 4, 1 (2023), 193–205.
American Psychiatric Association. 2016. DSM-5 Manual Diagnóstico e Estatístico de Transtornos Mentais. Grupo A. [link]
Aline dos Santos Moreira de Carvalho, Liliane Musumeci Ferreira, Léia Flauzina da Silva Albuquerque, Lucélia Aparecida Jagobucci, Karla de Lourdes Antunes Souza, Jean Carlos Triches, Luciana Menezes de Almeida, Ingrid dos Santos Pereira, Erika dos Santos Pereira, and Pacifico Ferraz Souto. 2022. The History of ADHD – Evolution. Research, Society and Development 11, 2 (Jan. 2022), e7611225604. DOI: 10.33448/rsd-v11i2.25604
Gabriel Ayres de Oliveira and Estêvão Coelho Teixeira. 2020. Pesquisa de Posição Ocular por Correpondência de Modelos para Renderização Baseada em Fóvea com FPGA. 2, 1 (2020).
Pijush Dutta, Shobhandeb Paul, Korhan Cengiz, Rishabh Anand, and Asok Kumar. 2023. Chapter 2 A predictive method for emotional sentiment analysis by deep learning from EEG of brainwave dataset. In Artificial Intelligence for Neurological Disorders, Ajith Abraham, Sujata Dash, Subhendu Kumar Pani, and Laura García-Hernández (Eds.). Academic Press, 25–48. DOI: 10.1016/B978-0-323-90277-9.00002X
Stephan Joachim Garbin, Oleg Komogortsev, Robert Cavin, Gregory Hughes, Yiru Shen, Immo Schuetz, and Sachin S Talathi. 2020. Dataset for eye tracking on a virtual reality platform. In ACM symposium on eye tracking research and applications. 1–10.
g.tec. 2024. Unicorn Hybrid Black. [link]
Xiaotong Gu, Zehong Cao, Alireza Jolfaei, Peng Xu, Dongrui Wu, Tzyy-Ping Jung, and Chin-Teng Lin. 2021. EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications. IEEE/ACM Transactions on Computational Biology and Bioinformatics 18, 5 (2021), 1645–1666. DOI: 10.1109/TCBB.2021.3052811
Ard Kastrati, Martyna Beata Płomecka, Damián Pascual, Lukas Wolf, Victor Gillioz, Roger Wattenhofer, and Nicolas Langer. 2021. EEGEyeNet: a simultaneous electroencephalography and eye-tracking dataset and benchmark for eye movement prediction. arXiv preprint arXiv:2111.05100 (2021).
Silvia Erika Kober and Christa Neuper. 2012. Using auditory event-related EEG potentials to assess presence in virtual reality. International Journal of Human-Computer Studies 70, 9 (2012), 577–587. DOI: 10.1016/j.ijhcs.2012.03.004
Henna Mäkinen, Elina Haavisto, Sara Havola, and Jaana-Maija Koivisto. 2022. User experiences of virtual reality technologies for healthcare in learning: an integrative review. Behaviour & Information Technology 41, 1 (2022), 1–17.
Meta. 2024. Meta Quest Pro. [link]
Adriana Mara Pimentel Maia Portugal and Renata Greco de Oliveira. 2020. UNIVERSIDADE E TDAH, ESTRATÉGIAS DE ENFRENTAMENTO: UMA REVISÃO SISTEMÁTICA. Revista Científica FACS 20, 25 (2020), 93–98.
Pranav Rajpurkar, Emma Chen, Oishi Banerjee, and Eric J. Topol. 2022. AI in health and medicine. Nature Medicine 28, 1 (01 Jan 2022), 31–38. DOI: 10.1038/s41591021-01614-0
Eduardo de Rezende. 2023. A história completa do TDAH Que Você Não conhecia. [link]
Henrique Souza Rossi. 2017. Imaginator: um sistema de realidade virtual para o auxílio no tratamento de transtornos de processamento sensorial. (2017).
Shashi Shekhar, Steven Feiner, and Walid Aref. 2015. Spatial Computing. Commun. ACM 59 (12 2015), 72–81. DOI: 10.1145/2756547
Drishti Yadav, Shilpee Yadav, and Karan Veer. 2020. A comprehensive assessment of Brain Computer Interfaces: Recent trends and challenges. Journal of Neuroscience Methods (2020). DOI: 10.1016/J.JNEUMETH.2020.108918
Ke Zhang and Ayse Begum Aslan. 2021. AI technologies for education: Recent research future directions. Computers and Education: Artificial Intelligence 2 (2021), 100025. DOI: 10.1016/j.caeai.2021.100025
