Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography

We investigated how well repetitive finger tapping movements can be decoded from scalp electroencephalography (EEG) signals. A linear decoder with memory was used to infer continuous index finger angular velocities from the low-pass filtered fluctuations of the amplitude of a plurality of EEG signal...

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Main Authors:	Paek, Andrew Y., Agashe, Harshavardhan A., Contreras-Vidal, José L.
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3952032/

id	pubmed-3952032
recordtype	oai_dc
spelling	pubmed-39520322014-03-21 Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography Paek, Andrew Y. Agashe, Harshavardhan A. Contreras-Vidal, José L. Neuroscience We investigated how well repetitive finger tapping movements can be decoded from scalp electroencephalography (EEG) signals. A linear decoder with memory was used to infer continuous index finger angular velocities from the low-pass filtered fluctuations of the amplitude of a plurality of EEG signals distributed across the scalp. To evaluate the accuracy of the decoder, the Pearson's correlation coefficient (r) between the observed and predicted trajectories was calculated in a 10-fold cross-validation scheme. We also assessed attempts to decode finger kinematics from EEG data that was cleaned with independent component analysis (ICA), EEG data from peripheral sensors, and EEG data from rest periods. A genetic algorithm (GA) was used to select combinations of EEG channels that maximized decoding accuracies. Our results (lower quartile r = 0.18, median r = 0.36, upper quartile r = 0.50) show that delta-band EEG signals contain useful information that can be used to infer finger kinematics. Further, the highest decoding accuracies were characterized by highly correlated delta band EEG activity mostly localized to the contralateral central areas of the scalp. Spectral analysis of EEG also showed bilateral alpha band (8–13 Hz) event related desynchronizations (ERDs) and contralateral beta band (20–30 Hz) event related synchronizations (ERSs) localized over central scalp areas. Overall, this study demonstrates the feasibility of decoding finger kinematics from scalp EEG signals. Frontiers Media S.A. 2014-03-13 /pmc/articles/PMC3952032/ /pubmed/24659964 http://dx.doi.org/10.3389/fneng.2014.00003 Text en Copyright © 2014 Paek, Agashe 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.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Paek, Andrew Y. Agashe, Harshavardhan A. Contreras-Vidal, José L.
spellingShingle	Paek, Andrew Y. Agashe, Harshavardhan A. Contreras-Vidal, José L. Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography
author_facet	Paek, Andrew Y. Agashe, Harshavardhan A. Contreras-Vidal, José L.
author_sort	Paek, Andrew Y.
title	Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography
title_short	Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography
title_full	Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography
title_fullStr	Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography
title_full_unstemmed	Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography
title_sort	decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography
description	We investigated how well repetitive finger tapping movements can be decoded from scalp electroencephalography (EEG) signals. A linear decoder with memory was used to infer continuous index finger angular velocities from the low-pass filtered fluctuations of the amplitude of a plurality of EEG signals distributed across the scalp. To evaluate the accuracy of the decoder, the Pearson's correlation coefficient (r) between the observed and predicted trajectories was calculated in a 10-fold cross-validation scheme. We also assessed attempts to decode finger kinematics from EEG data that was cleaned with independent component analysis (ICA), EEG data from peripheral sensors, and EEG data from rest periods. A genetic algorithm (GA) was used to select combinations of EEG channels that maximized decoding accuracies. Our results (lower quartile r = 0.18, median r = 0.36, upper quartile r = 0.50) show that delta-band EEG signals contain useful information that can be used to infer finger kinematics. Further, the highest decoding accuracies were characterized by highly correlated delta band EEG activity mostly localized to the contralateral central areas of the scalp. Spectral analysis of EEG also showed bilateral alpha band (8–13 Hz) event related desynchronizations (ERDs) and contralateral beta band (20–30 Hz) event related synchronizations (ERSs) localized over central scalp areas. Overall, this study demonstrates the feasibility of decoding finger kinematics from scalp EEG signals.
publisher	Frontiers Media S.A.
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3952032/
_version_	1612067035379924992

Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography

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