A novel noninvasive brain–computer interface by imagining isometric force levels

Abstract Physiological circuits differ across increasing isometric force levels during unilateral contraction. Therefore, we first explored the possibility of predicting the force level based on electroencephalogram (EEG) activity recorded during a single trial of unilateral 5% or 40% of maximal iso...
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Autor*in:	Hualiang, Li [verfasserIn] Xupeng, Ye Yuzhong, Liu Tingjun, Xie Wei, Tan Yali, Shen Qiru, Wang Chaolin, Xiong Yu, Wang Weilin, Lin Long, Jinyi

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Brain–computer interface (BCI) Electroencephalogram (EEG) Support vector machine (SVM) Isometric force Motor Imagination

Anmerkung:	© The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Cognitive neurodynamics - Dordrecht [u.a.] : Springer, 2007, 17(2022), 4 vom: 05. Sept., Seite 975-983
Übergeordnetes Werk:	volume:17 ; year:2022 ; number:4 ; day:05 ; month:09 ; pages:975-983

Links:	Volltext

DOI / URN:	10.1007/s11571-022-09875-2

Katalog-ID:	SPR052552098

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520			\|a Abstract Physiological circuits differ across increasing isometric force levels during unilateral contraction. Therefore, we first explored the possibility of predicting the force level based on electroencephalogram (EEG) activity recorded during a single trial of unilateral 5% or 40% of maximal isometric voluntary contraction (MVC) in right-hand grip imagination. Nine healthy subjects were involved in this study. The subjects were required to randomly perform 20 trials for each force level while imagining a right-hand grip. We proposed the use of common spatial patterns (CSPs) and coherence between EEG signals as features in a support vector machine for force level prediction. The results showed that the force levels could be predicted through single-trial EEGs while imagining the grip (mean accuracy = 81.4 ± 13.29%). Additionally, we tested the possibility of online control of a ball game using the above paradigm through unilateral grip imagination at different force levels (i.e., 5% of MVC imagination and 40% of MVC imagination for right-hand movement control). Subjects played the ball games effectively by controlling direction with our novel BCI system (n = 9, mean accuracy = 76.67 ± 9.35%). Data analysis validated the use of our BCI system in the online control of a ball game. This information may provide additional commands for the control of robots by users through combinations with other traditional brain–computer interfaces, e.g., different limb imaginations.
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700	1		\|a Weilin, Lin \|4 aut
700	1		\|a Long, Jinyi \|0 (orcid)0000-0001-6150-987X \|4 aut
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allfields	10.1007/s11571-022-09875-2 doi (DE-627)SPR052552098 (SPR)s11571-022-09875-2-e DE-627 ger DE-627 rakwb eng Hualiang, Li verfasserin aut A novel noninvasive brain–computer interface by imagining isometric force levels 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Physiological circuits differ across increasing isometric force levels during unilateral contraction. Therefore, we first explored the possibility of predicting the force level based on electroencephalogram (EEG) activity recorded during a single trial of unilateral 5% or 40% of maximal isometric voluntary contraction (MVC) in right-hand grip imagination. Nine healthy subjects were involved in this study. The subjects were required to randomly perform 20 trials for each force level while imagining a right-hand grip. We proposed the use of common spatial patterns (CSPs) and coherence between EEG signals as features in a support vector machine for force level prediction. The results showed that the force levels could be predicted through single-trial EEGs while imagining the grip (mean accuracy = 81.4 ± 13.29%). Additionally, we tested the possibility of online control of a ball game using the above paradigm through unilateral grip imagination at different force levels (i.e., 5% of MVC imagination and 40% of MVC imagination for right-hand movement control). Subjects played the ball games effectively by controlling direction with our novel BCI system (n = 9, mean accuracy = 76.67 ± 9.35%). Data analysis validated the use of our BCI system in the online control of a ball game. This information may provide additional commands for the control of robots by users through combinations with other traditional brain–computer interfaces, e.g., different limb imaginations. Brain–computer interface (BCI) (dpeaa)DE-He213 Electroencephalogram (EEG) (dpeaa)DE-He213 Support vector machine (SVM) (dpeaa)DE-He213 Isometric force (dpeaa)DE-He213 Motor Imagination (dpeaa)DE-He213 Xupeng, Ye aut Yuzhong, Liu aut Tingjun, Xie aut Wei, Tan aut Yali, Shen aut Qiru, Wang aut Chaolin, Xiong aut Yu, Wang aut Weilin, Lin aut Long, Jinyi (orcid)0000-0001-6150-987X aut Enthalten in Cognitive neurodynamics Dordrecht [u.a.] : Springer, 2007 17(2022), 4 vom: 05. Sept., Seite 975-983 (DE-627)527576689 (DE-600)2276890-7 1871-4099 nnns volume:17 year:2022 number:4 day:05 month:09 pages:975-983 https://dx.doi.org/10.1007/s11571-022-09875-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2022 4 05 09 975-983
spelling	10.1007/s11571-022-09875-2 doi (DE-627)SPR052552098 (SPR)s11571-022-09875-2-e DE-627 ger DE-627 rakwb eng Hualiang, Li verfasserin aut A novel noninvasive brain–computer interface by imagining isometric force levels 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Physiological circuits differ across increasing isometric force levels during unilateral contraction. Therefore, we first explored the possibility of predicting the force level based on electroencephalogram (EEG) activity recorded during a single trial of unilateral 5% or 40% of maximal isometric voluntary contraction (MVC) in right-hand grip imagination. Nine healthy subjects were involved in this study. The subjects were required to randomly perform 20 trials for each force level while imagining a right-hand grip. We proposed the use of common spatial patterns (CSPs) and coherence between EEG signals as features in a support vector machine for force level prediction. The results showed that the force levels could be predicted through single-trial EEGs while imagining the grip (mean accuracy = 81.4 ± 13.29%). Additionally, we tested the possibility of online control of a ball game using the above paradigm through unilateral grip imagination at different force levels (i.e., 5% of MVC imagination and 40% of MVC imagination for right-hand movement control). Subjects played the ball games effectively by controlling direction with our novel BCI system (n = 9, mean accuracy = 76.67 ± 9.35%). Data analysis validated the use of our BCI system in the online control of a ball game. This information may provide additional commands for the control of robots by users through combinations with other traditional brain–computer interfaces, e.g., different limb imaginations. Brain–computer interface (BCI) (dpeaa)DE-He213 Electroencephalogram (EEG) (dpeaa)DE-He213 Support vector machine (SVM) (dpeaa)DE-He213 Isometric force (dpeaa)DE-He213 Motor Imagination (dpeaa)DE-He213 Xupeng, Ye aut Yuzhong, Liu aut Tingjun, Xie aut Wei, Tan aut Yali, Shen aut Qiru, Wang aut Chaolin, Xiong aut Yu, Wang aut Weilin, Lin aut Long, Jinyi (orcid)0000-0001-6150-987X aut Enthalten in Cognitive neurodynamics Dordrecht [u.a.] : Springer, 2007 17(2022), 4 vom: 05. Sept., Seite 975-983 (DE-627)527576689 (DE-600)2276890-7 1871-4099 nnns volume:17 year:2022 number:4 day:05 month:09 pages:975-983 https://dx.doi.org/10.1007/s11571-022-09875-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2022 4 05 09 975-983
allfields_unstemmed	10.1007/s11571-022-09875-2 doi (DE-627)SPR052552098 (SPR)s11571-022-09875-2-e DE-627 ger DE-627 rakwb eng Hualiang, Li verfasserin aut A novel noninvasive brain–computer interface by imagining isometric force levels 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Physiological circuits differ across increasing isometric force levels during unilateral contraction. Therefore, we first explored the possibility of predicting the force level based on electroencephalogram (EEG) activity recorded during a single trial of unilateral 5% or 40% of maximal isometric voluntary contraction (MVC) in right-hand grip imagination. Nine healthy subjects were involved in this study. The subjects were required to randomly perform 20 trials for each force level while imagining a right-hand grip. We proposed the use of common spatial patterns (CSPs) and coherence between EEG signals as features in a support vector machine for force level prediction. The results showed that the force levels could be predicted through single-trial EEGs while imagining the grip (mean accuracy = 81.4 ± 13.29%). Additionally, we tested the possibility of online control of a ball game using the above paradigm through unilateral grip imagination at different force levels (i.e., 5% of MVC imagination and 40% of MVC imagination for right-hand movement control). Subjects played the ball games effectively by controlling direction with our novel BCI system (n = 9, mean accuracy = 76.67 ± 9.35%). Data analysis validated the use of our BCI system in the online control of a ball game. This information may provide additional commands for the control of robots by users through combinations with other traditional brain–computer interfaces, e.g., different limb imaginations. Brain–computer interface (BCI) (dpeaa)DE-He213 Electroencephalogram (EEG) (dpeaa)DE-He213 Support vector machine (SVM) (dpeaa)DE-He213 Isometric force (dpeaa)DE-He213 Motor Imagination (dpeaa)DE-He213 Xupeng, Ye aut Yuzhong, Liu aut Tingjun, Xie aut Wei, Tan aut Yali, Shen aut Qiru, Wang aut Chaolin, Xiong aut Yu, Wang aut Weilin, Lin aut Long, Jinyi (orcid)0000-0001-6150-987X aut Enthalten in Cognitive neurodynamics Dordrecht [u.a.] : Springer, 2007 17(2022), 4 vom: 05. Sept., Seite 975-983 (DE-627)527576689 (DE-600)2276890-7 1871-4099 nnns volume:17 year:2022 number:4 day:05 month:09 pages:975-983 https://dx.doi.org/10.1007/s11571-022-09875-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2022 4 05 09 975-983
allfieldsGer	10.1007/s11571-022-09875-2 doi (DE-627)SPR052552098 (SPR)s11571-022-09875-2-e DE-627 ger DE-627 rakwb eng Hualiang, Li verfasserin aut A novel noninvasive brain–computer interface by imagining isometric force levels 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Physiological circuits differ across increasing isometric force levels during unilateral contraction. Therefore, we first explored the possibility of predicting the force level based on electroencephalogram (EEG) activity recorded during a single trial of unilateral 5% or 40% of maximal isometric voluntary contraction (MVC) in right-hand grip imagination. Nine healthy subjects were involved in this study. The subjects were required to randomly perform 20 trials for each force level while imagining a right-hand grip. We proposed the use of common spatial patterns (CSPs) and coherence between EEG signals as features in a support vector machine for force level prediction. The results showed that the force levels could be predicted through single-trial EEGs while imagining the grip (mean accuracy = 81.4 ± 13.29%). Additionally, we tested the possibility of online control of a ball game using the above paradigm through unilateral grip imagination at different force levels (i.e., 5% of MVC imagination and 40% of MVC imagination for right-hand movement control). Subjects played the ball games effectively by controlling direction with our novel BCI system (n = 9, mean accuracy = 76.67 ± 9.35%). Data analysis validated the use of our BCI system in the online control of a ball game. This information may provide additional commands for the control of robots by users through combinations with other traditional brain–computer interfaces, e.g., different limb imaginations. Brain–computer interface (BCI) (dpeaa)DE-He213 Electroencephalogram (EEG) (dpeaa)DE-He213 Support vector machine (SVM) (dpeaa)DE-He213 Isometric force (dpeaa)DE-He213 Motor Imagination (dpeaa)DE-He213 Xupeng, Ye aut Yuzhong, Liu aut Tingjun, Xie aut Wei, Tan aut Yali, Shen aut Qiru, Wang aut Chaolin, Xiong aut Yu, Wang aut Weilin, Lin aut Long, Jinyi (orcid)0000-0001-6150-987X aut Enthalten in Cognitive neurodynamics Dordrecht [u.a.] : Springer, 2007 17(2022), 4 vom: 05. Sept., Seite 975-983 (DE-627)527576689 (DE-600)2276890-7 1871-4099 nnns volume:17 year:2022 number:4 day:05 month:09 pages:975-983 https://dx.doi.org/10.1007/s11571-022-09875-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2022 4 05 09 975-983
allfieldsSound	10.1007/s11571-022-09875-2 doi (DE-627)SPR052552098 (SPR)s11571-022-09875-2-e DE-627 ger DE-627 rakwb eng Hualiang, Li verfasserin aut A novel noninvasive brain–computer interface by imagining isometric force levels 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Physiological circuits differ across increasing isometric force levels during unilateral contraction. Therefore, we first explored the possibility of predicting the force level based on electroencephalogram (EEG) activity recorded during a single trial of unilateral 5% or 40% of maximal isometric voluntary contraction (MVC) in right-hand grip imagination. Nine healthy subjects were involved in this study. The subjects were required to randomly perform 20 trials for each force level while imagining a right-hand grip. We proposed the use of common spatial patterns (CSPs) and coherence between EEG signals as features in a support vector machine for force level prediction. The results showed that the force levels could be predicted through single-trial EEGs while imagining the grip (mean accuracy = 81.4 ± 13.29%). Additionally, we tested the possibility of online control of a ball game using the above paradigm through unilateral grip imagination at different force levels (i.e., 5% of MVC imagination and 40% of MVC imagination for right-hand movement control). Subjects played the ball games effectively by controlling direction with our novel BCI system (n = 9, mean accuracy = 76.67 ± 9.35%). Data analysis validated the use of our BCI system in the online control of a ball game. This information may provide additional commands for the control of robots by users through combinations with other traditional brain–computer interfaces, e.g., different limb imaginations. Brain–computer interface (BCI) (dpeaa)DE-He213 Electroencephalogram (EEG) (dpeaa)DE-He213 Support vector machine (SVM) (dpeaa)DE-He213 Isometric force (dpeaa)DE-He213 Motor Imagination (dpeaa)DE-He213 Xupeng, Ye aut Yuzhong, Liu aut Tingjun, Xie aut Wei, Tan aut Yali, Shen aut Qiru, Wang aut Chaolin, Xiong aut Yu, Wang aut Weilin, Lin aut Long, Jinyi (orcid)0000-0001-6150-987X aut Enthalten in Cognitive neurodynamics Dordrecht [u.a.] : Springer, 2007 17(2022), 4 vom: 05. Sept., Seite 975-983 (DE-627)527576689 (DE-600)2276890-7 1871-4099 nnns volume:17 year:2022 number:4 day:05 month:09 pages:975-983 https://dx.doi.org/10.1007/s11571-022-09875-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2022 4 05 09 975-983
language	English
source	Enthalten in Cognitive neurodynamics 17(2022), 4 vom: 05. Sept., Seite 975-983 volume:17 year:2022 number:4 day:05 month:09 pages:975-983
sourceStr	Enthalten in Cognitive neurodynamics 17(2022), 4 vom: 05. Sept., Seite 975-983 volume:17 year:2022 number:4 day:05 month:09 pages:975-983
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Brain–computer interface (BCI) Electroencephalogram (EEG) Support vector machine (SVM) Isometric force Motor Imagination
isfreeaccess_bool	false
container_title	Cognitive neurodynamics
authorswithroles_txt_mv	Hualiang, Li @@aut@@ Xupeng, Ye @@aut@@ Yuzhong, Liu @@aut@@ Tingjun, Xie @@aut@@ Wei, Tan @@aut@@ Yali, Shen @@aut@@ Qiru, Wang @@aut@@ Chaolin, Xiong @@aut@@ Yu, Wang @@aut@@ Weilin, Lin @@aut@@ Long, Jinyi @@aut@@
publishDateDaySort_date	2022-09-05T00:00:00Z
hierarchy_top_id	527576689
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language_de	englisch
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author	Hualiang, Li
spellingShingle	Hualiang, Li misc Brain–computer interface (BCI) misc Electroencephalogram (EEG) misc Support vector machine (SVM) misc Isometric force misc Motor Imagination A novel noninvasive brain–computer interface by imagining isometric force levels
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topic_title	A novel noninvasive brain–computer interface by imagining isometric force levels Brain–computer interface (BCI) (dpeaa)DE-He213 Electroencephalogram (EEG) (dpeaa)DE-He213 Support vector machine (SVM) (dpeaa)DE-He213 Isometric force (dpeaa)DE-He213 Motor Imagination (dpeaa)DE-He213
topic	misc Brain–computer interface (BCI) misc Electroencephalogram (EEG) misc Support vector machine (SVM) misc Isometric force misc Motor Imagination
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title	A novel noninvasive brain–computer interface by imagining isometric force levels
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title_full	A novel noninvasive brain–computer interface by imagining isometric force levels
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title_sort	novel noninvasive brain–computer interface by imagining isometric force levels
title_auth	A novel noninvasive brain–computer interface by imagining isometric force levels
abstract	Abstract Physiological circuits differ across increasing isometric force levels during unilateral contraction. Therefore, we first explored the possibility of predicting the force level based on electroencephalogram (EEG) activity recorded during a single trial of unilateral 5% or 40% of maximal isometric voluntary contraction (MVC) in right-hand grip imagination. Nine healthy subjects were involved in this study. The subjects were required to randomly perform 20 trials for each force level while imagining a right-hand grip. We proposed the use of common spatial patterns (CSPs) and coherence between EEG signals as features in a support vector machine for force level prediction. The results showed that the force levels could be predicted through single-trial EEGs while imagining the grip (mean accuracy = 81.4 ± 13.29%). Additionally, we tested the possibility of online control of a ball game using the above paradigm through unilateral grip imagination at different force levels (i.e., 5% of MVC imagination and 40% of MVC imagination for right-hand movement control). Subjects played the ball games effectively by controlling direction with our novel BCI system (n = 9, mean accuracy = 76.67 ± 9.35%). Data analysis validated the use of our BCI system in the online control of a ball game. This information may provide additional commands for the control of robots by users through combinations with other traditional brain–computer interfaces, e.g., different limb imaginations. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Physiological circuits differ across increasing isometric force levels during unilateral contraction. Therefore, we first explored the possibility of predicting the force level based on electroencephalogram (EEG) activity recorded during a single trial of unilateral 5% or 40% of maximal isometric voluntary contraction (MVC) in right-hand grip imagination. Nine healthy subjects were involved in this study. The subjects were required to randomly perform 20 trials for each force level while imagining a right-hand grip. We proposed the use of common spatial patterns (CSPs) and coherence between EEG signals as features in a support vector machine for force level prediction. The results showed that the force levels could be predicted through single-trial EEGs while imagining the grip (mean accuracy = 81.4 ± 13.29%). Additionally, we tested the possibility of online control of a ball game using the above paradigm through unilateral grip imagination at different force levels (i.e., 5% of MVC imagination and 40% of MVC imagination for right-hand movement control). Subjects played the ball games effectively by controlling direction with our novel BCI system (n = 9, mean accuracy = 76.67 ± 9.35%). Data analysis validated the use of our BCI system in the online control of a ball game. This information may provide additional commands for the control of robots by users through combinations with other traditional brain–computer interfaces, e.g., different limb imaginations. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Physiological circuits differ across increasing isometric force levels during unilateral contraction. Therefore, we first explored the possibility of predicting the force level based on electroencephalogram (EEG) activity recorded during a single trial of unilateral 5% or 40% of maximal isometric voluntary contraction (MVC) in right-hand grip imagination. Nine healthy subjects were involved in this study. The subjects were required to randomly perform 20 trials for each force level while imagining a right-hand grip. We proposed the use of common spatial patterns (CSPs) and coherence between EEG signals as features in a support vector machine for force level prediction. The results showed that the force levels could be predicted through single-trial EEGs while imagining the grip (mean accuracy = 81.4 ± 13.29%). Additionally, we tested the possibility of online control of a ball game using the above paradigm through unilateral grip imagination at different force levels (i.e., 5% of MVC imagination and 40% of MVC imagination for right-hand movement control). Subjects played the ball games effectively by controlling direction with our novel BCI system (n = 9, mean accuracy = 76.67 ± 9.35%). Data analysis validated the use of our BCI system in the online control of a ball game. This information may provide additional commands for the control of robots by users through combinations with other traditional brain–computer interfaces, e.g., different limb imaginations. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	A novel noninvasive brain–computer interface by imagining isometric force levels
url	https://dx.doi.org/10.1007/s11571-022-09875-2
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author2	Xupeng, Ye Yuzhong, Liu Tingjun, Xie Wei, Tan Yali, Shen Qiru, Wang Chaolin, Xiong Yu, Wang Weilin, Lin Long, Jinyi
author2Str	Xupeng, Ye Yuzhong, Liu Tingjun, Xie Wei, Tan Yali, Shen Qiru, Wang Chaolin, Xiong Yu, Wang Weilin, Lin Long, Jinyi
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doi_str	10.1007/s11571-022-09875-2
up_date	2024-07-04T03:12:01.475Z
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