Cargando…
Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution
Low frequency signals recorded from non-invasive electroencephalography (EEG), in particular movement-related cortical potentials (MRPs), are associated with preparation and execution of movement and thus present a target for use in brain-machine interfaces. We investigated the ability to decode mov...
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/PMC4243562/ https://www.ncbi.nlm.nih.gov/pubmed/25505377 http://dx.doi.org/10.3389/fnins.2014.00376 |
_version_ | 1782346118311444480 |
---|---|
author | Bulea, Thomas C. Prasad, Saurabh Kilicarslan, Atilla Contreras-Vidal, Jose L. |
author_facet | Bulea, Thomas C. Prasad, Saurabh Kilicarslan, Atilla Contreras-Vidal, Jose L. |
author_sort | Bulea, Thomas C. |
collection | PubMed |
description | Low frequency signals recorded from non-invasive electroencephalography (EEG), in particular movement-related cortical potentials (MRPs), are associated with preparation and execution of movement and thus present a target for use in brain-machine interfaces. We investigated the ability to decode movement intent from delta-band (0.1–4 Hz) EEG recorded immediately before movement execution in healthy volunteers. We used data from epochs starting 1.5 s before movement onset to classify future movements into one of three classes: stand-up, sit-down, or quiet. We assessed classification accuracy in both externally triggered and self-paced paradigms. Movement onset was determined from electromyography (EMG) recordings synchronized with EEG signals. We employed an artifact subspace reconstruction (ASR) algorithm to eliminate high amplitude noise before building our time-embedded EEG features. We applied local Fisher's discriminant analysis to reduce the dimensionality of our spatio-temporal features and subsequently used a Gaussian mixture model classifier for our three class problem. Our results demonstrate significantly better than chance classification accuracy (chance level = 33.3%) for the self-initiated (78.0 ± 2.6%) and triggered (74.7 ± 5.7%) paradigms. Surprisingly, we found no significant difference in classification accuracy between the self-paced and cued paradigms when using the full set of non-peripheral electrodes. However, accuracy was significantly increased for self-paced movements when only electrodes over the primary motor area were used. Overall, this study demonstrates that delta-band EEG recorded immediately before movement carries discriminative information regarding movement type. Our results suggest that EEG-based classifiers could improve lower-limb neuroprostheses and neurorehabilitation techniques by providing earlier detection of movement intent, which could be used in robot-assisted strategies for motor training and recovery of function. |
format | Online Article Text |
id | pubmed-4243562 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-42435622014-12-10 Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution Bulea, Thomas C. Prasad, Saurabh Kilicarslan, Atilla Contreras-Vidal, Jose L. Front Neurosci Neuroscience Low frequency signals recorded from non-invasive electroencephalography (EEG), in particular movement-related cortical potentials (MRPs), are associated with preparation and execution of movement and thus present a target for use in brain-machine interfaces. We investigated the ability to decode movement intent from delta-band (0.1–4 Hz) EEG recorded immediately before movement execution in healthy volunteers. We used data from epochs starting 1.5 s before movement onset to classify future movements into one of three classes: stand-up, sit-down, or quiet. We assessed classification accuracy in both externally triggered and self-paced paradigms. Movement onset was determined from electromyography (EMG) recordings synchronized with EEG signals. We employed an artifact subspace reconstruction (ASR) algorithm to eliminate high amplitude noise before building our time-embedded EEG features. We applied local Fisher's discriminant analysis to reduce the dimensionality of our spatio-temporal features and subsequently used a Gaussian mixture model classifier for our three class problem. Our results demonstrate significantly better than chance classification accuracy (chance level = 33.3%) for the self-initiated (78.0 ± 2.6%) and triggered (74.7 ± 5.7%) paradigms. Surprisingly, we found no significant difference in classification accuracy between the self-paced and cued paradigms when using the full set of non-peripheral electrodes. However, accuracy was significantly increased for self-paced movements when only electrodes over the primary motor area were used. Overall, this study demonstrates that delta-band EEG recorded immediately before movement carries discriminative information regarding movement type. Our results suggest that EEG-based classifiers could improve lower-limb neuroprostheses and neurorehabilitation techniques by providing earlier detection of movement intent, which could be used in robot-assisted strategies for motor training and recovery of function. Frontiers Media S.A. 2014-11-25 /pmc/articles/PMC4243562/ /pubmed/25505377 http://dx.doi.org/10.3389/fnins.2014.00376 Text en Copyright © 2014 Bulea, Prasad, Kilicarslan and Contreras-Vidal. 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 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 Bulea, Thomas C. Prasad, Saurabh Kilicarslan, Atilla Contreras-Vidal, Jose L. Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution |
title | Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution |
title_full | Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution |
title_fullStr | Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution |
title_full_unstemmed | Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution |
title_short | Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution |
title_sort | sitting and standing intention can be decoded from scalp eeg recorded prior to movement execution |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4243562/ https://www.ncbi.nlm.nih.gov/pubmed/25505377 http://dx.doi.org/10.3389/fnins.2014.00376 |
work_keys_str_mv | AT buleathomasc sittingandstandingintentioncanbedecodedfromscalpeegrecordedpriortomovementexecution AT prasadsaurabh sittingandstandingintentioncanbedecodedfromscalpeegrecordedpriortomovementexecution AT kilicarslanatilla sittingandstandingintentioncanbedecodedfromscalpeegrecordedpriortomovementexecution AT contrerasvidaljosel sittingandstandingintentioncanbedecodedfromscalpeegrecordedpriortomovementexecution |