Decoding individual finger movements from one hand using human EEG signals.

Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimens...

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Autores principales:	Ke Liao, Ran Xiao, Jania Gonzalez, Lei Ding
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/66fc51577d9a4195b52ea0a5d6f2fc24
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spelling	oai:doaj.org-article:66fc51577d9a4195b52ea0a5d6f2fc242021-11-18T08:38:24ZDecoding individual finger movements from one hand using human EEG signals.1932-620310.1371/journal.pone.0085192https://doaj.org/article/66fc51577d9a4195b52ea0a5d6f2fc242014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24416360/?tool=EBIhttps://doaj.org/toc/1932-6203Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.Ke LiaoRan XiaoJania GonzalezLei DingPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 1, p e85192 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ke Liao Ran Xiao Jania Gonzalez Lei Ding Decoding individual finger movements from one hand using human EEG signals.
description	Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.
format	article
author	Ke Liao Ran Xiao Jania Gonzalez Lei Ding
author_facet	Ke Liao Ran Xiao Jania Gonzalez Lei Ding
author_sort	Ke Liao
title	Decoding individual finger movements from one hand using human EEG signals.
title_short	Decoding individual finger movements from one hand using human EEG signals.
title_full	Decoding individual finger movements from one hand using human EEG signals.
title_fullStr	Decoding individual finger movements from one hand using human EEG signals.
title_full_unstemmed	Decoding individual finger movements from one hand using human EEG signals.
title_sort	decoding individual finger movements from one hand using human eeg signals.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/66fc51577d9a4195b52ea0a5d6f2fc24
work_keys_str_mv	AT keliao decodingindividualfingermovementsfromonehandusinghumaneegsignals AT ranxiao decodingindividualfingermovementsfromonehandusinghumaneegsignals AT janiagonzalez decodingindividualfingermovementsfromonehandusinghumaneegsignals AT leiding decodingindividualfingermovementsfromonehandusinghumaneegsignals
_version_	1718421504070254592

Decoding individual finger movements from one hand using human EEG signals.

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