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Individual temporal and spatial dynamics of learning to control central Beta activity in neurofeedback training
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
2023 (English)In: 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER), IEEE, 2023, article id 23160432Conference paper, Published paper (Refereed)
Abstract [en]

Neurofeedback (NFB) and Brain-Computer Interface (BCI) research seldom present within-session individual learning dynamics. This is even though a large proportion of NFB and BCI users cannot learn neural self-regulation required to control the feedback. Understanding the time course and learning variability between participants might allow us to design better NFB and BCI protocols to promote learning of neural self-regulation. The importance of developing novel NFB and BCI protocols becomes apparent, considering the clinical utility of these techniques. Tuning the brain to perform optimally could provide for long-term non-pharmacological treatment without any drug-associated side effects. This paper reports the strategies used by participants and the individual dynamics of central Beta NFB downregulation training and associated mental strategies for nine participants. The results showed that all participants could learn to downregulate their central Beta power in a single session, however, the dynamics of learning differed between participants. We visually identified two learning dynamics; 1) a continual decrease in Beta power and 2) an initial decrease followed by a stable level of Beta power. Topographic plots indicated high spatial variability in Beta power decreases in participants. Responses from end-of-session debriefing indicated that all participants felt they could control the feedback. Although participants could control the feedback, an optimal mental strategy for controlling central Beta power was not revealed.

Place, publisher, year, edition, pages
IEEE, 2023. article id 23160432
Keywords [en]
Training, Protocols, Neural engineering, Feedback amplifiers, Brain-computer interfaces, Neurofeedback, Tuning
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-64844DOI: 10.1109/ner52421.2023.10123781ISI: 001009053700066Scopus ID: 2-s2.0-8516062899ISBN: 978-1-6654-6292-1 (print)OAI: oai:DiVA.org:mdh-64844DiVA, id: diva2:1815288
Conference
2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)
Available from: 2023-11-28 Created: 2023-11-28 Last updated: 2023-12-04Bibliographically approved

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Åstrand, Elaine

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
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  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf