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Estimating cognitive load during self-regulation of brain activity and neurofeedback with therapeutic brain-computer interfaces
Neurofeedback (NFB) training with brain-computer interfaces (BCIs) is currently being studied in a variety of neurological and neuropsychiatric conditions in an aim to reduce disorder-specific symptoms. For this purpose, a range of classification algorithms has been explored to identify different br...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2015
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4329795/ https://www.ncbi.nlm.nih.gov/pubmed/25762908 http://dx.doi.org/10.3389/fnbeh.2015.00021 |
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author | Bauer, Robert Gharabaghi, Alireza |
author_facet | Bauer, Robert Gharabaghi, Alireza |
author_sort | Bauer, Robert |
collection | PubMed |
description | Neurofeedback (NFB) training with brain-computer interfaces (BCIs) is currently being studied in a variety of neurological and neuropsychiatric conditions in an aim to reduce disorder-specific symptoms. For this purpose, a range of classification algorithms has been explored to identify different brain states. These neural states, e.g., self-regulated brain activity vs. rest, are separated by setting a threshold parameter. Measures such as the maximum classification accuracy (CA) have been introduced to evaluate the performance of these algorithms. Interestingly enough, precisely these measures are often used to estimate the subject’s ability to perform brain self-regulation. This is surprising, given that the goal of improving the tool that differentiates between brain states is different from the aim of optimizing NFB for the subject performing brain self-regulation. For the latter, knowledge about mental resources and work load is essential in order to adapt the difficulty of the intervention accordingly. In this context, we apply an analytical method and provide empirical data to determine the zone of proximal development (ZPD) as a measure of a subject’s cognitive resources and the instructional efficacy of NFB. This approach is based on a reconsideration of item-response theory (IRT) and cognitive load theory for instructional design, and combines them with the CA curve to provide a measure of BCI performance. |
format | Online Article Text |
id | pubmed-4329795 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-43297952015-03-11 Estimating cognitive load during self-regulation of brain activity and neurofeedback with therapeutic brain-computer interfaces Bauer, Robert Gharabaghi, Alireza Front Behav Neurosci Neuroscience Neurofeedback (NFB) training with brain-computer interfaces (BCIs) is currently being studied in a variety of neurological and neuropsychiatric conditions in an aim to reduce disorder-specific symptoms. For this purpose, a range of classification algorithms has been explored to identify different brain states. These neural states, e.g., self-regulated brain activity vs. rest, are separated by setting a threshold parameter. Measures such as the maximum classification accuracy (CA) have been introduced to evaluate the performance of these algorithms. Interestingly enough, precisely these measures are often used to estimate the subject’s ability to perform brain self-regulation. This is surprising, given that the goal of improving the tool that differentiates between brain states is different from the aim of optimizing NFB for the subject performing brain self-regulation. For the latter, knowledge about mental resources and work load is essential in order to adapt the difficulty of the intervention accordingly. In this context, we apply an analytical method and provide empirical data to determine the zone of proximal development (ZPD) as a measure of a subject’s cognitive resources and the instructional efficacy of NFB. This approach is based on a reconsideration of item-response theory (IRT) and cognitive load theory for instructional design, and combines them with the CA curve to provide a measure of BCI performance. Frontiers Media S.A. 2015-02-16 /pmc/articles/PMC4329795/ /pubmed/25762908 http://dx.doi.org/10.3389/fnbeh.2015.00021 Text en Copyright © 2015 Bauer and Gharabaghi. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Bauer, Robert Gharabaghi, Alireza Estimating cognitive load during self-regulation of brain activity and neurofeedback with therapeutic brain-computer interfaces |
title | Estimating cognitive load during self-regulation of brain activity and neurofeedback with therapeutic brain-computer interfaces |
title_full | Estimating cognitive load during self-regulation of brain activity and neurofeedback with therapeutic brain-computer interfaces |
title_fullStr | Estimating cognitive load during self-regulation of brain activity and neurofeedback with therapeutic brain-computer interfaces |
title_full_unstemmed | Estimating cognitive load during self-regulation of brain activity and neurofeedback with therapeutic brain-computer interfaces |
title_short | Estimating cognitive load during self-regulation of brain activity and neurofeedback with therapeutic brain-computer interfaces |
title_sort | estimating cognitive load during self-regulation of brain activity and neurofeedback with therapeutic brain-computer interfaces |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4329795/ https://www.ncbi.nlm.nih.gov/pubmed/25762908 http://dx.doi.org/10.3389/fnbeh.2015.00021 |
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