Cargando…
Neural decoding of expressive human movement from scalp electroencephalography (EEG)
Although efforts to characterize human movement through electroencephalography (EEG) have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such m...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3986521/ https://www.ncbi.nlm.nih.gov/pubmed/24782734 http://dx.doi.org/10.3389/fnhum.2014.00188 |
_version_ | 1782311722709680128 |
---|---|
author | Cruz-Garza, Jesus G. Hernandez, Zachery R. Nepaul, Sargoon Bradley, Karen K. Contreras-Vidal, Jose L. |
author_facet | Cruz-Garza, Jesus G. Hernandez, Zachery R. Nepaul, Sargoon Bradley, Karen K. Contreras-Vidal, Jose L. |
author_sort | Cruz-Garza, Jesus G. |
collection | PubMed |
description | Although efforts to characterize human movement through electroencephalography (EEG) have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA) dancers. Each dancer performed whole body movements of three Action types: movements devoid of expressive qualities (“Neutral”), non-expressive movements while thinking about specific expressive qualities (“Think”), and enacted expressive movements (“Do”). The expressive movement qualities that were used in the “Think” and “Do” actions consisted of a sequence of eight Laban Effort qualities as defined by LMA—a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2–4 Hz) EEG as input to a machine learning algorithm that computed locality-preserving Fisher's discriminant analysis (LFDA) for dimensionality reduction followed by Gaussian mixture models (GMMs) to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort quality (giving a total of 17 classes). Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort quality Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of neuroprosthetics to restore movements. |
format | Online Article Text |
id | pubmed-3986521 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-39865212014-04-29 Neural decoding of expressive human movement from scalp electroencephalography (EEG) Cruz-Garza, Jesus G. Hernandez, Zachery R. Nepaul, Sargoon Bradley, Karen K. Contreras-Vidal, Jose L. Front Hum Neurosci Neuroscience Although efforts to characterize human movement through electroencephalography (EEG) have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA) dancers. Each dancer performed whole body movements of three Action types: movements devoid of expressive qualities (“Neutral”), non-expressive movements while thinking about specific expressive qualities (“Think”), and enacted expressive movements (“Do”). The expressive movement qualities that were used in the “Think” and “Do” actions consisted of a sequence of eight Laban Effort qualities as defined by LMA—a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2–4 Hz) EEG as input to a machine learning algorithm that computed locality-preserving Fisher's discriminant analysis (LFDA) for dimensionality reduction followed by Gaussian mixture models (GMMs) to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort quality (giving a total of 17 classes). Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort quality Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of neuroprosthetics to restore movements. Frontiers Media S.A. 2014-04-08 /pmc/articles/PMC3986521/ /pubmed/24782734 http://dx.doi.org/10.3389/fnhum.2014.00188 Text en Copyright © 2014 Cruz-Garza, Hernandez, Nepaul, Bradley and Contreras-Vidal. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or 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 Cruz-Garza, Jesus G. Hernandez, Zachery R. Nepaul, Sargoon Bradley, Karen K. Contreras-Vidal, Jose L. Neural decoding of expressive human movement from scalp electroencephalography (EEG) |
title | Neural decoding of expressive human movement from scalp electroencephalography (EEG) |
title_full | Neural decoding of expressive human movement from scalp electroencephalography (EEG) |
title_fullStr | Neural decoding of expressive human movement from scalp electroencephalography (EEG) |
title_full_unstemmed | Neural decoding of expressive human movement from scalp electroencephalography (EEG) |
title_short | Neural decoding of expressive human movement from scalp electroencephalography (EEG) |
title_sort | neural decoding of expressive human movement from scalp electroencephalography (eeg) |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3986521/ https://www.ncbi.nlm.nih.gov/pubmed/24782734 http://dx.doi.org/10.3389/fnhum.2014.00188 |
work_keys_str_mv | AT cruzgarzajesusg neuraldecodingofexpressivehumanmovementfromscalpelectroencephalographyeeg AT hernandezzacheryr neuraldecodingofexpressivehumanmovementfromscalpelectroencephalographyeeg AT nepaulsargoon neuraldecodingofexpressivehumanmovementfromscalpelectroencephalographyeeg AT bradleykarenk neuraldecodingofexpressivehumanmovementfromscalpelectroencephalographyeeg AT contrerasvidaljosel neuraldecodingofexpressivehumanmovementfromscalpelectroencephalographyeeg |