Cargando…
Artifacts in EEG-Based BCI Therapies: Friend or Foe?
EEG-based brain–computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with speci...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747566/ https://www.ncbi.nlm.nih.gov/pubmed/35009639 http://dx.doi.org/10.3390/s22010096 |
_version_ | 1784630864457498624 |
---|---|
author | McDermott, Eric James Raggam, Philipp Kirsch, Sven Belardinelli, Paolo Ziemann, Ulf Zrenner, Christoph |
author_facet | McDermott, Eric James Raggam, Philipp Kirsch, Sven Belardinelli, Paolo Ziemann, Ulf Zrenner, Christoph |
author_sort | McDermott, Eric James |
collection | PubMed |
description | EEG-based brain–computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts, such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data were collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While informative brain signals can be recovered with suitable cleaning procedures, we recommend that features should not be designed solely to maximize classification accuracy, as this could select for remaining artifactual components. We also propose the use of machine learning approaches that are interpretable to verify that classification is driven by physiological brain states. In summary, whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states. |
format | Online Article Text |
id | pubmed-8747566 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87475662022-01-11 Artifacts in EEG-Based BCI Therapies: Friend or Foe? McDermott, Eric James Raggam, Philipp Kirsch, Sven Belardinelli, Paolo Ziemann, Ulf Zrenner, Christoph Sensors (Basel) Article EEG-based brain–computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts, such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data were collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While informative brain signals can be recovered with suitable cleaning procedures, we recommend that features should not be designed solely to maximize classification accuracy, as this could select for remaining artifactual components. We also propose the use of machine learning approaches that are interpretable to verify that classification is driven by physiological brain states. In summary, whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states. MDPI 2021-12-24 /pmc/articles/PMC8747566/ /pubmed/35009639 http://dx.doi.org/10.3390/s22010096 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article McDermott, Eric James Raggam, Philipp Kirsch, Sven Belardinelli, Paolo Ziemann, Ulf Zrenner, Christoph Artifacts in EEG-Based BCI Therapies: Friend or Foe? |
title | Artifacts in EEG-Based BCI Therapies: Friend or Foe? |
title_full | Artifacts in EEG-Based BCI Therapies: Friend or Foe? |
title_fullStr | Artifacts in EEG-Based BCI Therapies: Friend or Foe? |
title_full_unstemmed | Artifacts in EEG-Based BCI Therapies: Friend or Foe? |
title_short | Artifacts in EEG-Based BCI Therapies: Friend or Foe? |
title_sort | artifacts in eeg-based bci therapies: friend or foe? |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747566/ https://www.ncbi.nlm.nih.gov/pubmed/35009639 http://dx.doi.org/10.3390/s22010096 |
work_keys_str_mv | AT mcdermottericjames artifactsineegbasedbcitherapiesfriendorfoe AT raggamphilipp artifactsineegbasedbcitherapiesfriendorfoe AT kirschsven artifactsineegbasedbcitherapiesfriendorfoe AT belardinellipaolo artifactsineegbasedbcitherapiesfriendorfoe AT ziemannulf artifactsineegbasedbcitherapiesfriendorfoe AT zrennerchristoph artifactsineegbasedbcitherapiesfriendorfoe |