Brain-Computer Interface Training of mu EEG Rhythms in Intellectually Impaired Children with Autism: A Feasibility Case Series.

Autor: LaMarca K; Vista, CA, USA. kristenlamarca@outlook.com.; Department of Clinical Psychology, California School of Professional Psychology, Alliant University, San Diego, USA. kristenlamarca@outlook.com., Gevirtz R; Department of Clinical Psychology, California School of Professional Psychology, Alliant University, San Diego, USA., Lincoln AJ; Department of Clinical Psychology, California School of Professional Psychology, Alliant University, San Diego, USA., Pineda JA; Department of Cognitive Neuroscience, University of California, San Diego, USA.
Jazyk: angličtina
Zdroj: Applied psychophysiology and biofeedback [Appl Psychophysiol Biofeedback] 2023 Jun; Vol. 48 (2), pp. 229-245. Date of Electronic Publication: 2023 Jan 06.
DOI: 10.1007/s10484-022-09576-w
Abstrakt: Prior studies show that neurofeedback training (NFT) of mu rhythms improves behavior and EEG mu rhythm suppression during action observation in children with autism spectrum disorder (ASD). However, intellectually impaired persons were excluded because of their behavioral challenges. We aimed to determine if intellectually impaired children with ASD, who were behaviorally prepared to take part in a mu-NFT study using conditioned auditory reinforcers, would show improvements in symptoms and mu suppression following mu-NFT. Seven children with ASD (ages 6-8; mean IQ 70.6 ± 7.5) successfully took part in mu-NFT. Four cases demonstrated positive learning trends (hit rates) during mu-NFT (learners), and three cases did not (non-learners). Artifact-creating behaviors were present during tests of mu suppression for all cases, but were more frequent in non-learners. Following NFT, learners showed behavioral improvements and were more likely to show evidence of a short-term increase in mu suppression relative to non-learners who showed little to no EEG or behavior improvements. Results support mu-NFT's application in some children who otherwise may not have been able to take part without enhanced behavioral preparations. Children who have more limitations in demonstrating learning during NFT, or in providing data with relatively low artifact during task-dependent EEG tests, may have less chance of benefiting from mu-NFT. Improving the identification of ideal mu-NFT candidates, mu-NFT learning rates, source analyses, EEG outcome task performance, population-specific artifact-rejection methods, and the theoretical bases of NFT protocols, could aid future BCI-based, neurorehabilitation efforts.
(© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje