Influence of EEG channel reduction on lower limb motor imagery during electrical stimulation in healthy and paraplegic subjects
Purpose Among a wide range of applications that make use of Brain-Computer Interfaces (BCIs), the pattern recognition of motor imagery (MI) to trigger neuromodulation systems in the control of functional movements has received increasing attention. In this work, we evaluate the effect of reducing th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Júnior, Paulo Broniera [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© Sociedade Brasileira de Engenharia Biomedica 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Research on biomedical engineering - [Cham] : Springer International Publishing, 2015, 38(2022), 2 vom: 28. Jan., Seite 689-699 |
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| Übergeordnetes Werk: |
volume:38 ; year:2022 ; number:2 ; day:28 ; month:01 ; pages:689-699 |
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| DOI / URN: |
10.1007/s42600-021-00189-6 |
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| Katalog-ID: |
SPR046997865 |
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| 100 | 1 | |a Júnior, Paulo Broniera |e verfasserin |0 (orcid)0000-0001-9857-6208 |4 aut | |
| 245 | 1 | 0 | |a Influence of EEG channel reduction on lower limb motor imagery during electrical stimulation in healthy and paraplegic subjects |
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| 520 | |a Purpose Among a wide range of applications that make use of Brain-Computer Interfaces (BCIs), the pattern recognition of motor imagery (MI) to trigger neuromodulation systems in the control of functional movements has received increasing attention. In this work, we evaluate the effect of reducing the number of electroencephalography (EEG) channels in the performance of lower limbs’ motor imagery classification during the application of electrical stimulation (ES) in 20 Hz (ES20Hz), 35 Hz (ES35Hz), and 50 Hz (ES50Hz). Methods Five subjects participated in the study, three healthy participants (average age of 28 years old) and two paraplegic volunteers, 43 and 47 years old, respectively. In total, each participant performed 90 repetitions of motor imagery of the lower limb with 11 EEG channels (10-10 configuration) under electrical stimulation. After the data acquisition, a systematic and artificial reduction in the number of EEG channels (decreasing from 11 to 1 and considering all cases $11, 10, {\dots } , 2, 1$) was applied to evaluate the offline classifiers. The pattern classification was performed using the following methods: (i) linear discriminant analysis (LDA), (ii) multilayer perceptron (MLP), and (iii) support vector machine (SVM). The accuracy performance of 11 different configurations regarding the EEG channels was obtained and studied. Results The highest accuracy (86.5%) was obtained with the SVM classifier. There was no significant difference in the accuracy (median ± interquartile range) obtained with the 11-EEG channel configuration (SVM = ES20Hz: 78.50% ± 8.18%, ES35Hz: 77.80% ± 7.15%, ES50Hz: 75.80% ± 5.17% and LDA = ES20Hz: 69.40% ± 6.35%, ES35Hz: 69.00% ± 3.90%, ES50Hz: 67.30% ± 5.35% ) and the 4-EEG channel configuration (SVM = ES20Hz: 72.10% ± 6.38%, ES35Hz: 72.20% ± 6.42%, ES50Hz: 68.20% ± 5.07% and LDA = ES20Hz: 63.30% ± 3.58%, ES35Hz: 63.60% ± 3.83%, ES50Hz: 60.46% ± 5.65%). Conclusion These results are important for future neuroprosthesis implementations, as they indicate the possibility of simpler and more compact assistive technology control systems, being the main contribution of this work. | ||
| 650 | 4 | |a EEG classification |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Motor imagery |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Campos, Daniel Prado |4 aut | |
| 700 | 1 | |a Lazzaretti, André Eugênio |4 aut | |
| 700 | 1 | |a Nohama, Percy |4 aut | |
| 700 | 1 | |a Carvalho, Aparecido Augusto |4 aut | |
| 700 | 1 | |a Krueger, Eddy |4 aut | |
| 700 | 1 | |a Teixeira, Marcelo Carvalho Minhoto |4 aut | |
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10.1007/s42600-021-00189-6 doi (DE-627)SPR046997865 (SPR)s42600-021-00189-6-e DE-627 ger DE-627 rakwb eng Júnior, Paulo Broniera verfasserin (orcid)0000-0001-9857-6208 aut Influence of EEG channel reduction on lower limb motor imagery during electrical stimulation in healthy and paraplegic subjects 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sociedade Brasileira de Engenharia Biomedica 2022 Purpose Among a wide range of applications that make use of Brain-Computer Interfaces (BCIs), the pattern recognition of motor imagery (MI) to trigger neuromodulation systems in the control of functional movements has received increasing attention. In this work, we evaluate the effect of reducing the number of electroencephalography (EEG) channels in the performance of lower limbs’ motor imagery classification during the application of electrical stimulation (ES) in 20 Hz (ES20Hz), 35 Hz (ES35Hz), and 50 Hz (ES50Hz). Methods Five subjects participated in the study, three healthy participants (average age of 28 years old) and two paraplegic volunteers, 43 and 47 years old, respectively. In total, each participant performed 90 repetitions of motor imagery of the lower limb with 11 EEG channels (10-10 configuration) under electrical stimulation. After the data acquisition, a systematic and artificial reduction in the number of EEG channels (decreasing from 11 to 1 and considering all cases $11, 10, {\dots } , 2, 1$) was applied to evaluate the offline classifiers. The pattern classification was performed using the following methods: (i) linear discriminant analysis (LDA), (ii) multilayer perceptron (MLP), and (iii) support vector machine (SVM). The accuracy performance of 11 different configurations regarding the EEG channels was obtained and studied. Results The highest accuracy (86.5%) was obtained with the SVM classifier. There was no significant difference in the accuracy (median ± interquartile range) obtained with the 11-EEG channel configuration (SVM = ES20Hz: 78.50% ± 8.18%, ES35Hz: 77.80% ± 7.15%, ES50Hz: 75.80% ± 5.17% and LDA = ES20Hz: 69.40% ± 6.35%, ES35Hz: 69.00% ± 3.90%, ES50Hz: 67.30% ± 5.35% ) and the 4-EEG channel configuration (SVM = ES20Hz: 72.10% ± 6.38%, ES35Hz: 72.20% ± 6.42%, ES50Hz: 68.20% ± 5.07% and LDA = ES20Hz: 63.30% ± 3.58%, ES35Hz: 63.60% ± 3.83%, ES50Hz: 60.46% ± 5.65%). Conclusion These results are important for future neuroprosthesis implementations, as they indicate the possibility of simpler and more compact assistive technology control systems, being the main contribution of this work. EEG classification (dpeaa)DE-He213 Motor imagery (dpeaa)DE-He213 Signal classification (dpeaa)DE-He213 Campos, Daniel Prado aut Lazzaretti, André Eugênio aut Nohama, Percy aut Carvalho, Aparecido Augusto aut Krueger, Eddy aut Teixeira, Marcelo Carvalho Minhoto aut Enthalten in Research on biomedical engineering [Cham] : Springer International Publishing, 2015 38(2022), 2 vom: 28. Jan., Seite 689-699 (DE-627)890513783 (DE-600)2897414-1 2446-4740 nnns volume:38 year:2022 number:2 day:28 month:01 pages:689-699 https://dx.doi.org/10.1007/s42600-021-00189-6 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_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_4393 GBV_ILN_4700 AR 38 2022 2 28 01 689-699 |
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10.1007/s42600-021-00189-6 doi (DE-627)SPR046997865 (SPR)s42600-021-00189-6-e DE-627 ger DE-627 rakwb eng Júnior, Paulo Broniera verfasserin (orcid)0000-0001-9857-6208 aut Influence of EEG channel reduction on lower limb motor imagery during electrical stimulation in healthy and paraplegic subjects 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sociedade Brasileira de Engenharia Biomedica 2022 Purpose Among a wide range of applications that make use of Brain-Computer Interfaces (BCIs), the pattern recognition of motor imagery (MI) to trigger neuromodulation systems in the control of functional movements has received increasing attention. In this work, we evaluate the effect of reducing the number of electroencephalography (EEG) channels in the performance of lower limbs’ motor imagery classification during the application of electrical stimulation (ES) in 20 Hz (ES20Hz), 35 Hz (ES35Hz), and 50 Hz (ES50Hz). Methods Five subjects participated in the study, three healthy participants (average age of 28 years old) and two paraplegic volunteers, 43 and 47 years old, respectively. In total, each participant performed 90 repetitions of motor imagery of the lower limb with 11 EEG channels (10-10 configuration) under electrical stimulation. After the data acquisition, a systematic and artificial reduction in the number of EEG channels (decreasing from 11 to 1 and considering all cases $11, 10, {\dots } , 2, 1$) was applied to evaluate the offline classifiers. The pattern classification was performed using the following methods: (i) linear discriminant analysis (LDA), (ii) multilayer perceptron (MLP), and (iii) support vector machine (SVM). The accuracy performance of 11 different configurations regarding the EEG channels was obtained and studied. Results The highest accuracy (86.5%) was obtained with the SVM classifier. There was no significant difference in the accuracy (median ± interquartile range) obtained with the 11-EEG channel configuration (SVM = ES20Hz: 78.50% ± 8.18%, ES35Hz: 77.80% ± 7.15%, ES50Hz: 75.80% ± 5.17% and LDA = ES20Hz: 69.40% ± 6.35%, ES35Hz: 69.00% ± 3.90%, ES50Hz: 67.30% ± 5.35% ) and the 4-EEG channel configuration (SVM = ES20Hz: 72.10% ± 6.38%, ES35Hz: 72.20% ± 6.42%, ES50Hz: 68.20% ± 5.07% and LDA = ES20Hz: 63.30% ± 3.58%, ES35Hz: 63.60% ± 3.83%, ES50Hz: 60.46% ± 5.65%). Conclusion These results are important for future neuroprosthesis implementations, as they indicate the possibility of simpler and more compact assistive technology control systems, being the main contribution of this work. EEG classification (dpeaa)DE-He213 Motor imagery (dpeaa)DE-He213 Signal classification (dpeaa)DE-He213 Campos, Daniel Prado aut Lazzaretti, André Eugênio aut Nohama, Percy aut Carvalho, Aparecido Augusto aut Krueger, Eddy aut Teixeira, Marcelo Carvalho Minhoto aut Enthalten in Research on biomedical engineering [Cham] : Springer International Publishing, 2015 38(2022), 2 vom: 28. Jan., Seite 689-699 (DE-627)890513783 (DE-600)2897414-1 2446-4740 nnns volume:38 year:2022 number:2 day:28 month:01 pages:689-699 https://dx.doi.org/10.1007/s42600-021-00189-6 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_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_4393 GBV_ILN_4700 AR 38 2022 2 28 01 689-699 |
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10.1007/s42600-021-00189-6 doi (DE-627)SPR046997865 (SPR)s42600-021-00189-6-e DE-627 ger DE-627 rakwb eng Júnior, Paulo Broniera verfasserin (orcid)0000-0001-9857-6208 aut Influence of EEG channel reduction on lower limb motor imagery during electrical stimulation in healthy and paraplegic subjects 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sociedade Brasileira de Engenharia Biomedica 2022 Purpose Among a wide range of applications that make use of Brain-Computer Interfaces (BCIs), the pattern recognition of motor imagery (MI) to trigger neuromodulation systems in the control of functional movements has received increasing attention. In this work, we evaluate the effect of reducing the number of electroencephalography (EEG) channels in the performance of lower limbs’ motor imagery classification during the application of electrical stimulation (ES) in 20 Hz (ES20Hz), 35 Hz (ES35Hz), and 50 Hz (ES50Hz). Methods Five subjects participated in the study, three healthy participants (average age of 28 years old) and two paraplegic volunteers, 43 and 47 years old, respectively. In total, each participant performed 90 repetitions of motor imagery of the lower limb with 11 EEG channels (10-10 configuration) under electrical stimulation. After the data acquisition, a systematic and artificial reduction in the number of EEG channels (decreasing from 11 to 1 and considering all cases $11, 10, {\dots } , 2, 1$) was applied to evaluate the offline classifiers. The pattern classification was performed using the following methods: (i) linear discriminant analysis (LDA), (ii) multilayer perceptron (MLP), and (iii) support vector machine (SVM). The accuracy performance of 11 different configurations regarding the EEG channels was obtained and studied. Results The highest accuracy (86.5%) was obtained with the SVM classifier. There was no significant difference in the accuracy (median ± interquartile range) obtained with the 11-EEG channel configuration (SVM = ES20Hz: 78.50% ± 8.18%, ES35Hz: 77.80% ± 7.15%, ES50Hz: 75.80% ± 5.17% and LDA = ES20Hz: 69.40% ± 6.35%, ES35Hz: 69.00% ± 3.90%, ES50Hz: 67.30% ± 5.35% ) and the 4-EEG channel configuration (SVM = ES20Hz: 72.10% ± 6.38%, ES35Hz: 72.20% ± 6.42%, ES50Hz: 68.20% ± 5.07% and LDA = ES20Hz: 63.30% ± 3.58%, ES35Hz: 63.60% ± 3.83%, ES50Hz: 60.46% ± 5.65%). Conclusion These results are important for future neuroprosthesis implementations, as they indicate the possibility of simpler and more compact assistive technology control systems, being the main contribution of this work. EEG classification (dpeaa)DE-He213 Motor imagery (dpeaa)DE-He213 Signal classification (dpeaa)DE-He213 Campos, Daniel Prado aut Lazzaretti, André Eugênio aut Nohama, Percy aut Carvalho, Aparecido Augusto aut Krueger, Eddy aut Teixeira, Marcelo Carvalho Minhoto aut Enthalten in Research on biomedical engineering [Cham] : Springer International Publishing, 2015 38(2022), 2 vom: 28. Jan., Seite 689-699 (DE-627)890513783 (DE-600)2897414-1 2446-4740 nnns volume:38 year:2022 number:2 day:28 month:01 pages:689-699 https://dx.doi.org/10.1007/s42600-021-00189-6 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_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_4393 GBV_ILN_4700 AR 38 2022 2 28 01 689-699 |
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10.1007/s42600-021-00189-6 doi (DE-627)SPR046997865 (SPR)s42600-021-00189-6-e DE-627 ger DE-627 rakwb eng Júnior, Paulo Broniera verfasserin (orcid)0000-0001-9857-6208 aut Influence of EEG channel reduction on lower limb motor imagery during electrical stimulation in healthy and paraplegic subjects 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sociedade Brasileira de Engenharia Biomedica 2022 Purpose Among a wide range of applications that make use of Brain-Computer Interfaces (BCIs), the pattern recognition of motor imagery (MI) to trigger neuromodulation systems in the control of functional movements has received increasing attention. In this work, we evaluate the effect of reducing the number of electroencephalography (EEG) channels in the performance of lower limbs’ motor imagery classification during the application of electrical stimulation (ES) in 20 Hz (ES20Hz), 35 Hz (ES35Hz), and 50 Hz (ES50Hz). Methods Five subjects participated in the study, three healthy participants (average age of 28 years old) and two paraplegic volunteers, 43 and 47 years old, respectively. In total, each participant performed 90 repetitions of motor imagery of the lower limb with 11 EEG channels (10-10 configuration) under electrical stimulation. After the data acquisition, a systematic and artificial reduction in the number of EEG channels (decreasing from 11 to 1 and considering all cases $11, 10, {\dots } , 2, 1$) was applied to evaluate the offline classifiers. The pattern classification was performed using the following methods: (i) linear discriminant analysis (LDA), (ii) multilayer perceptron (MLP), and (iii) support vector machine (SVM). The accuracy performance of 11 different configurations regarding the EEG channels was obtained and studied. Results The highest accuracy (86.5%) was obtained with the SVM classifier. There was no significant difference in the accuracy (median ± interquartile range) obtained with the 11-EEG channel configuration (SVM = ES20Hz: 78.50% ± 8.18%, ES35Hz: 77.80% ± 7.15%, ES50Hz: 75.80% ± 5.17% and LDA = ES20Hz: 69.40% ± 6.35%, ES35Hz: 69.00% ± 3.90%, ES50Hz: 67.30% ± 5.35% ) and the 4-EEG channel configuration (SVM = ES20Hz: 72.10% ± 6.38%, ES35Hz: 72.20% ± 6.42%, ES50Hz: 68.20% ± 5.07% and LDA = ES20Hz: 63.30% ± 3.58%, ES35Hz: 63.60% ± 3.83%, ES50Hz: 60.46% ± 5.65%). Conclusion These results are important for future neuroprosthesis implementations, as they indicate the possibility of simpler and more compact assistive technology control systems, being the main contribution of this work. EEG classification (dpeaa)DE-He213 Motor imagery (dpeaa)DE-He213 Signal classification (dpeaa)DE-He213 Campos, Daniel Prado aut Lazzaretti, André Eugênio aut Nohama, Percy aut Carvalho, Aparecido Augusto aut Krueger, Eddy aut Teixeira, Marcelo Carvalho Minhoto aut Enthalten in Research on biomedical engineering [Cham] : Springer International Publishing, 2015 38(2022), 2 vom: 28. Jan., Seite 689-699 (DE-627)890513783 (DE-600)2897414-1 2446-4740 nnns volume:38 year:2022 number:2 day:28 month:01 pages:689-699 https://dx.doi.org/10.1007/s42600-021-00189-6 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_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_4393 GBV_ILN_4700 AR 38 2022 2 28 01 689-699 |
| allfieldsSound |
10.1007/s42600-021-00189-6 doi (DE-627)SPR046997865 (SPR)s42600-021-00189-6-e DE-627 ger DE-627 rakwb eng Júnior, Paulo Broniera verfasserin (orcid)0000-0001-9857-6208 aut Influence of EEG channel reduction on lower limb motor imagery during electrical stimulation in healthy and paraplegic subjects 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sociedade Brasileira de Engenharia Biomedica 2022 Purpose Among a wide range of applications that make use of Brain-Computer Interfaces (BCIs), the pattern recognition of motor imagery (MI) to trigger neuromodulation systems in the control of functional movements has received increasing attention. In this work, we evaluate the effect of reducing the number of electroencephalography (EEG) channels in the performance of lower limbs’ motor imagery classification during the application of electrical stimulation (ES) in 20 Hz (ES20Hz), 35 Hz (ES35Hz), and 50 Hz (ES50Hz). Methods Five subjects participated in the study, three healthy participants (average age of 28 years old) and two paraplegic volunteers, 43 and 47 years old, respectively. In total, each participant performed 90 repetitions of motor imagery of the lower limb with 11 EEG channels (10-10 configuration) under electrical stimulation. After the data acquisition, a systematic and artificial reduction in the number of EEG channels (decreasing from 11 to 1 and considering all cases $11, 10, {\dots } , 2, 1$) was applied to evaluate the offline classifiers. The pattern classification was performed using the following methods: (i) linear discriminant analysis (LDA), (ii) multilayer perceptron (MLP), and (iii) support vector machine (SVM). The accuracy performance of 11 different configurations regarding the EEG channels was obtained and studied. Results The highest accuracy (86.5%) was obtained with the SVM classifier. There was no significant difference in the accuracy (median ± interquartile range) obtained with the 11-EEG channel configuration (SVM = ES20Hz: 78.50% ± 8.18%, ES35Hz: 77.80% ± 7.15%, ES50Hz: 75.80% ± 5.17% and LDA = ES20Hz: 69.40% ± 6.35%, ES35Hz: 69.00% ± 3.90%, ES50Hz: 67.30% ± 5.35% ) and the 4-EEG channel configuration (SVM = ES20Hz: 72.10% ± 6.38%, ES35Hz: 72.20% ± 6.42%, ES50Hz: 68.20% ± 5.07% and LDA = ES20Hz: 63.30% ± 3.58%, ES35Hz: 63.60% ± 3.83%, ES50Hz: 60.46% ± 5.65%). Conclusion These results are important for future neuroprosthesis implementations, as they indicate the possibility of simpler and more compact assistive technology control systems, being the main contribution of this work. EEG classification (dpeaa)DE-He213 Motor imagery (dpeaa)DE-He213 Signal classification (dpeaa)DE-He213 Campos, Daniel Prado aut Lazzaretti, André Eugênio aut Nohama, Percy aut Carvalho, Aparecido Augusto aut Krueger, Eddy aut Teixeira, Marcelo Carvalho Minhoto aut Enthalten in Research on biomedical engineering [Cham] : Springer International Publishing, 2015 38(2022), 2 vom: 28. Jan., Seite 689-699 (DE-627)890513783 (DE-600)2897414-1 2446-4740 nnns volume:38 year:2022 number:2 day:28 month:01 pages:689-699 https://dx.doi.org/10.1007/s42600-021-00189-6 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_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_4393 GBV_ILN_4700 AR 38 2022 2 28 01 689-699 |
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Enthalten in Research on biomedical engineering 38(2022), 2 vom: 28. Jan., Seite 689-699 volume:38 year:2022 number:2 day:28 month:01 pages:689-699 |
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Júnior, Paulo Broniera @@aut@@ Campos, Daniel Prado @@aut@@ Lazzaretti, André Eugênio @@aut@@ Nohama, Percy @@aut@@ Carvalho, Aparecido Augusto @@aut@@ Krueger, Eddy @@aut@@ Teixeira, Marcelo Carvalho Minhoto @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR046997865</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230509102113.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220514s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s42600-021-00189-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR046997865</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s42600-021-00189-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Júnior, Paulo Broniera</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-9857-6208</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Influence of EEG channel reduction on lower limb motor imagery during electrical stimulation in healthy and paraplegic subjects</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Sociedade Brasileira de Engenharia Biomedica 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Purpose Among a wide range of applications that make use of Brain-Computer Interfaces (BCIs), the pattern recognition of motor imagery (MI) to trigger neuromodulation systems in the control of functional movements has received increasing attention. In this work, we evaluate the effect of reducing the number of electroencephalography (EEG) channels in the performance of lower limbs’ motor imagery classification during the application of electrical stimulation (ES) in 20 Hz (ES20Hz), 35 Hz (ES35Hz), and 50 Hz (ES50Hz). Methods Five subjects participated in the study, three healthy participants (average age of 28 years old) and two paraplegic volunteers, 43 and 47 years old, respectively. In total, each participant performed 90 repetitions of motor imagery of the lower limb with 11 EEG channels (10-10 configuration) under electrical stimulation. After the data acquisition, a systematic and artificial reduction in the number of EEG channels (decreasing from 11 to 1 and considering all cases $11, 10, {\dots } , 2, 1$) was applied to evaluate the offline classifiers. The pattern classification was performed using the following methods: (i) linear discriminant analysis (LDA), (ii) multilayer perceptron (MLP), and (iii) support vector machine (SVM). The accuracy performance of 11 different configurations regarding the EEG channels was obtained and studied. Results The highest accuracy (86.5%) was obtained with the SVM classifier. There was no significant difference in the accuracy (median ± interquartile range) obtained with the 11-EEG channel configuration (SVM = ES20Hz: 78.50% ± 8.18%, ES35Hz: 77.80% ± 7.15%, ES50Hz: 75.80% ± 5.17% and LDA = ES20Hz: 69.40% ± 6.35%, ES35Hz: 69.00% ± 3.90%, ES50Hz: 67.30% ± 5.35% ) and the 4-EEG channel configuration (SVM = ES20Hz: 72.10% ± 6.38%, ES35Hz: 72.20% ± 6.42%, ES50Hz: 68.20% ± 5.07% and LDA = ES20Hz: 63.30% ± 3.58%, ES35Hz: 63.60% ± 3.83%, ES50Hz: 60.46% ± 5.65%). Conclusion These results are important for future neuroprosthesis implementations, as they indicate the possibility of simpler and more compact assistive technology control systems, being the main contribution of this work.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">EEG classification</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Motor imagery</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Signal classification</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Campos, Daniel Prado</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lazzaretti, André Eugênio</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nohama, Percy</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Carvalho, Aparecido Augusto</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Krueger, Eddy</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Teixeira, Marcelo Carvalho Minhoto</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Research on biomedical engineering</subfield><subfield code="d">[Cham] : Springer International Publishing, 2015</subfield><subfield code="g">38(2022), 2 vom: 28. 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Júnior, Paulo Broniera |
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Júnior, Paulo Broniera misc EEG classification misc Motor imagery misc Signal classification Influence of EEG channel reduction on lower limb motor imagery during electrical stimulation in healthy and paraplegic subjects |
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Influence of EEG channel reduction on lower limb motor imagery during electrical stimulation in healthy and paraplegic subjects EEG classification (dpeaa)DE-He213 Motor imagery (dpeaa)DE-He213 Signal classification (dpeaa)DE-He213 |
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Influence of EEG channel reduction on lower limb motor imagery during electrical stimulation in healthy and paraplegic subjects |
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Influence of EEG channel reduction on lower limb motor imagery during electrical stimulation in healthy and paraplegic subjects |
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Júnior, Paulo Broniera |
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Júnior, Paulo Broniera Campos, Daniel Prado Lazzaretti, André Eugênio Nohama, Percy Carvalho, Aparecido Augusto Krueger, Eddy Teixeira, Marcelo Carvalho Minhoto |
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influence of eeg channel reduction on lower limb motor imagery during electrical stimulation in healthy and paraplegic subjects |
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Influence of EEG channel reduction on lower limb motor imagery during electrical stimulation in healthy and paraplegic subjects |
| abstract |
Purpose Among a wide range of applications that make use of Brain-Computer Interfaces (BCIs), the pattern recognition of motor imagery (MI) to trigger neuromodulation systems in the control of functional movements has received increasing attention. In this work, we evaluate the effect of reducing the number of electroencephalography (EEG) channels in the performance of lower limbs’ motor imagery classification during the application of electrical stimulation (ES) in 20 Hz (ES20Hz), 35 Hz (ES35Hz), and 50 Hz (ES50Hz). Methods Five subjects participated in the study, three healthy participants (average age of 28 years old) and two paraplegic volunteers, 43 and 47 years old, respectively. In total, each participant performed 90 repetitions of motor imagery of the lower limb with 11 EEG channels (10-10 configuration) under electrical stimulation. After the data acquisition, a systematic and artificial reduction in the number of EEG channels (decreasing from 11 to 1 and considering all cases $11, 10, {\dots } , 2, 1$) was applied to evaluate the offline classifiers. The pattern classification was performed using the following methods: (i) linear discriminant analysis (LDA), (ii) multilayer perceptron (MLP), and (iii) support vector machine (SVM). The accuracy performance of 11 different configurations regarding the EEG channels was obtained and studied. Results The highest accuracy (86.5%) was obtained with the SVM classifier. There was no significant difference in the accuracy (median ± interquartile range) obtained with the 11-EEG channel configuration (SVM = ES20Hz: 78.50% ± 8.18%, ES35Hz: 77.80% ± 7.15%, ES50Hz: 75.80% ± 5.17% and LDA = ES20Hz: 69.40% ± 6.35%, ES35Hz: 69.00% ± 3.90%, ES50Hz: 67.30% ± 5.35% ) and the 4-EEG channel configuration (SVM = ES20Hz: 72.10% ± 6.38%, ES35Hz: 72.20% ± 6.42%, ES50Hz: 68.20% ± 5.07% and LDA = ES20Hz: 63.30% ± 3.58%, ES35Hz: 63.60% ± 3.83%, ES50Hz: 60.46% ± 5.65%). Conclusion These results are important for future neuroprosthesis implementations, as they indicate the possibility of simpler and more compact assistive technology control systems, being the main contribution of this work. © Sociedade Brasileira de Engenharia Biomedica 2022 |
| abstractGer |
Purpose Among a wide range of applications that make use of Brain-Computer Interfaces (BCIs), the pattern recognition of motor imagery (MI) to trigger neuromodulation systems in the control of functional movements has received increasing attention. In this work, we evaluate the effect of reducing the number of electroencephalography (EEG) channels in the performance of lower limbs’ motor imagery classification during the application of electrical stimulation (ES) in 20 Hz (ES20Hz), 35 Hz (ES35Hz), and 50 Hz (ES50Hz). Methods Five subjects participated in the study, three healthy participants (average age of 28 years old) and two paraplegic volunteers, 43 and 47 years old, respectively. In total, each participant performed 90 repetitions of motor imagery of the lower limb with 11 EEG channels (10-10 configuration) under electrical stimulation. After the data acquisition, a systematic and artificial reduction in the number of EEG channels (decreasing from 11 to 1 and considering all cases $11, 10, {\dots } , 2, 1$) was applied to evaluate the offline classifiers. The pattern classification was performed using the following methods: (i) linear discriminant analysis (LDA), (ii) multilayer perceptron (MLP), and (iii) support vector machine (SVM). The accuracy performance of 11 different configurations regarding the EEG channels was obtained and studied. Results The highest accuracy (86.5%) was obtained with the SVM classifier. There was no significant difference in the accuracy (median ± interquartile range) obtained with the 11-EEG channel configuration (SVM = ES20Hz: 78.50% ± 8.18%, ES35Hz: 77.80% ± 7.15%, ES50Hz: 75.80% ± 5.17% and LDA = ES20Hz: 69.40% ± 6.35%, ES35Hz: 69.00% ± 3.90%, ES50Hz: 67.30% ± 5.35% ) and the 4-EEG channel configuration (SVM = ES20Hz: 72.10% ± 6.38%, ES35Hz: 72.20% ± 6.42%, ES50Hz: 68.20% ± 5.07% and LDA = ES20Hz: 63.30% ± 3.58%, ES35Hz: 63.60% ± 3.83%, ES50Hz: 60.46% ± 5.65%). Conclusion These results are important for future neuroprosthesis implementations, as they indicate the possibility of simpler and more compact assistive technology control systems, being the main contribution of this work. © Sociedade Brasileira de Engenharia Biomedica 2022 |
| abstract_unstemmed |
Purpose Among a wide range of applications that make use of Brain-Computer Interfaces (BCIs), the pattern recognition of motor imagery (MI) to trigger neuromodulation systems in the control of functional movements has received increasing attention. In this work, we evaluate the effect of reducing the number of electroencephalography (EEG) channels in the performance of lower limbs’ motor imagery classification during the application of electrical stimulation (ES) in 20 Hz (ES20Hz), 35 Hz (ES35Hz), and 50 Hz (ES50Hz). Methods Five subjects participated in the study, three healthy participants (average age of 28 years old) and two paraplegic volunteers, 43 and 47 years old, respectively. In total, each participant performed 90 repetitions of motor imagery of the lower limb with 11 EEG channels (10-10 configuration) under electrical stimulation. After the data acquisition, a systematic and artificial reduction in the number of EEG channels (decreasing from 11 to 1 and considering all cases $11, 10, {\dots } , 2, 1$) was applied to evaluate the offline classifiers. The pattern classification was performed using the following methods: (i) linear discriminant analysis (LDA), (ii) multilayer perceptron (MLP), and (iii) support vector machine (SVM). The accuracy performance of 11 different configurations regarding the EEG channels was obtained and studied. Results The highest accuracy (86.5%) was obtained with the SVM classifier. There was no significant difference in the accuracy (median ± interquartile range) obtained with the 11-EEG channel configuration (SVM = ES20Hz: 78.50% ± 8.18%, ES35Hz: 77.80% ± 7.15%, ES50Hz: 75.80% ± 5.17% and LDA = ES20Hz: 69.40% ± 6.35%, ES35Hz: 69.00% ± 3.90%, ES50Hz: 67.30% ± 5.35% ) and the 4-EEG channel configuration (SVM = ES20Hz: 72.10% ± 6.38%, ES35Hz: 72.20% ± 6.42%, ES50Hz: 68.20% ± 5.07% and LDA = ES20Hz: 63.30% ± 3.58%, ES35Hz: 63.60% ± 3.83%, ES50Hz: 60.46% ± 5.65%). Conclusion These results are important for future neuroprosthesis implementations, as they indicate the possibility of simpler and more compact assistive technology control systems, being the main contribution of this work. © Sociedade Brasileira de Engenharia Biomedica 2022 |
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Influence of EEG channel reduction on lower limb motor imagery during electrical stimulation in healthy and paraplegic subjects |
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https://dx.doi.org/10.1007/s42600-021-00189-6 |
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Campos, Daniel Prado Lazzaretti, André Eugênio Nohama, Percy Carvalho, Aparecido Augusto Krueger, Eddy Teixeira, Marcelo Carvalho Minhoto |
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Campos, Daniel Prado Lazzaretti, André Eugênio Nohama, Percy Carvalho, Aparecido Augusto Krueger, Eddy Teixeira, Marcelo Carvalho Minhoto |
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2024-07-04T01:23:44.246Z |
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| score |
7.3998346 |