Classification of opening/closing hand motor imagery induced by left and right robotic gloves through EEG signals
This study presents a novel strategy for classifying Motor Imagery (MI) related to hand opening/closing actions using electroencephalography signals. This approach combines the passive motion induced by a robotic glove and action observation. Two groups of subjects executed a protocol based on left...
- Autores:
-
Gonzalez Cely, Aura Ximena
Blanco-Diaz, Cristian Felipe
Guerrero Mendez, Cristian David
Villa Parra, Ana Cecilia
Bastos-Filho, Teodiano Freire
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2024
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/13534
- Acceso en línea:
- https://doi.org/10.32397/tesea.vol5.n2.579
- Palabra clave:
- MI-BCI
Upper-limb
Classification
Motor Imagery
Robotic Glove
- Rights
- openAccess
- License
- https://creativecommons.org/licenses/by/4.0
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| dc.title.spa.fl_str_mv |
Classification of opening/closing hand motor imagery induced by left and right robotic gloves through EEG signals |
| dc.title.translated.spa.fl_str_mv |
Classification of opening/closing hand motor imagery induced by left and right robotic gloves through EEG signals |
| title |
Classification of opening/closing hand motor imagery induced by left and right robotic gloves through EEG signals |
| spellingShingle |
Classification of opening/closing hand motor imagery induced by left and right robotic gloves through EEG signals MI-BCI Upper-limb Classification Motor Imagery Robotic Glove |
| title_short |
Classification of opening/closing hand motor imagery induced by left and right robotic gloves through EEG signals |
| title_full |
Classification of opening/closing hand motor imagery induced by left and right robotic gloves through EEG signals |
| title_fullStr |
Classification of opening/closing hand motor imagery induced by left and right robotic gloves through EEG signals |
| title_full_unstemmed |
Classification of opening/closing hand motor imagery induced by left and right robotic gloves through EEG signals |
| title_sort |
Classification of opening/closing hand motor imagery induced by left and right robotic gloves through EEG signals |
| dc.creator.fl_str_mv |
Gonzalez Cely, Aura Ximena Blanco-Diaz, Cristian Felipe Guerrero Mendez, Cristian David Villa Parra, Ana Cecilia Bastos-Filho, Teodiano Freire |
| dc.contributor.author.eng.fl_str_mv |
Gonzalez Cely, Aura Ximena Blanco-Diaz, Cristian Felipe Guerrero Mendez, Cristian David Villa Parra, Ana Cecilia Bastos-Filho, Teodiano Freire |
| dc.subject.eng.fl_str_mv |
MI-BCI Upper-limb Classification Motor Imagery Robotic Glove |
| topic |
MI-BCI Upper-limb Classification Motor Imagery Robotic Glove |
| description |
This study presents a novel strategy for classifying Motor Imagery (MI) related to hand opening/closing actions using electroencephalography signals. This approach combines the passive motion induced by a robotic glove and action observation. Two groups of subjects executed a protocol based on left and right hand movement MI to address this. Subsequently, spectral features were used on $mu$ and $beta$ bands, and machine-learning algorithms were used for classification. The results showed better performance for right-hand motion recognition using k-Nearest Neighbors (kNN), which achieved the highest performance metrics of 0.71, 0.76, and 0.28 for Accuracy (ACC), true positive rate, and false positive rate, respectively. These findings demonstrate the feasibility of the proposed methodology for improving the recognition of MI tasks of the same limb, which can contribute to the design of more robust brain-computer interfaces for the enhancement of rehabilitation therapy for post-stroke patients. |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-12-24 00:00:00 |
| dc.date.available.none.fl_str_mv |
2024-12-24 00:00:00 |
| dc.date.issued.none.fl_str_mv |
2024-12-24 |
| dc.type.spa.fl_str_mv |
Artículo de revista |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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Journal article |
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Text |
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info:eu-repo/semantics/publishedVersion |
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https://doi.org/10.32397/tesea.vol5.n2.579 |
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10.32397/tesea.vol5.n2.579 |
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2745-0120 |
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10.32397/tesea.vol5.n2.579 2745-0120 |
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eng |
| language |
eng |
| dc.relation.references.eng.fl_str_mv |
Muhammad Ahmed Khan, Rig Das, Helle K. Iversen, and Sadasivan Puthusserypady. Review on motor imagery based bci systems for upper limb post-stroke neurorehabilitation: From designing to application. Computers in Biology and Medicine, 123:103843, 2020. [2] Tácia Cotinguiba Machado, Adriani Andrade Carregosa, Matheus S Santos, Nildo Manoel da Silva Ribeiro, and Ailton Melo. Efficacy of motor imagery additional to motor-based therapy in the recovery of motor function of the upper limb in post-stroke individuals: a systematic review. Topics in stroke rehabilitation, 26(7):548–553, 2019. [3] Bin Gu, Kun Wang, Long Chen, Jiatong He, Dingze Zhang, Minpeng Xu, Zhongpeng Wang, and Dong Ming. Study of the correlation between the motor ability of the individual upper limbs and motor imagery induced neural activities. Neuroscience, 530:56– 65, 2023. [4] Nicholas Cheng, Kok Soon Phua, Hwa Sen Lai, Pui Kit Tam, Ka Yin Tang, Kai Kei Cheng, Raye Chen-Hua Yeow, Kai Keng Ang, Cuntai Guan, and Jeong Hoon Lim. Brain-computer interface-based soft robotic glove rehabilitation for stroke. IEEE Transactions on Biomedical Engineering, 67(12):3339–3351, 2020. [5] Ning Guo, Xiaojun Wang, Dehao Duanmu, Xin Huang, Xiaodong Li, Yunli Fan, Hailan Li, Yongquan Liu, Eric Hiu Kwong Yeung, Michael Kai Tsun To, Jianxiong Gu, Feng Wan, and Yong Hu. Ssvep-based brain computer interface controlled soft robotic glove for post-stroke hand function rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30:1737–1744, 2022. [6] T.F. Bastos-Filho, A C Villa-Parra, C.D. Guerrero-Méndez, A X González-Cely, C F Blanco-Díaz, D. Delisle-Rodríguez, and T. Igasaki. A novel methodology based on static visual stimuli and kinesthetic motor imagery for upper limb neurorehabilitation. Research on Biomedical Engineering, page 1–14, 2024. [7] Ruyi Foong, Kai Keng Ang, Chai Quek, Cuntai Guan, Kok Soon Phua, Christopher Wee Keong Kuah, Vishwanath Arun Deshmukh, Lester Hon Lum Yam, Deshan Kumar Rajeswaran, Ning Tang, et al. Assessment of the efficacy of eeg-based mi-bci with visual feedback and eeg correlates of mental fatigue for upper-limb stroke rehabilitation. IEEE Transactions on Biomedical Engineering, 67(3):786–795, 2019. [8] Tadashi Yamamoto and Toyohiro Hamaguchi. Development of an application that implements a brain–computer interface to an upper-limb motor assistance robot to facilitate active exercise in patients: A feasibility study. Applied Sciences (Switzerland), 13(17), 2023. [9] Juan Sebastián Ramírez Archila and Alvaro David Orjuela-Cañón. Machine learning techniques for detecting motor imagery in upper limbs. In 2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020), pages 1–5, 2020. [10] Wing-Kin Tam, Kai-yu Tong, Fei Meng, and Shangkai Gao. A minimal set of electrodes for motor imagery bci to control an assistive device in chronic stroke subjects: A multi-session study. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(6):617–627, 2011. [11] Mahyar Tajdini, Volodymyr Sokolov, Ievgeniia Kuzminykh, Stavros Shiaeles, and Bogdan Ghita. Wireless sensors for brain activity—a survey. Electronics (Switzerland), 9(12):1– 26, 2020. [12] Christa Neuper, Reinhold Scherer, Miriam Reiner, and Gert Pfurtscheller. Imagery of motor actions: Differential effects of kinesthetic and visual–motor mode of imagery in single-trial eeg. Cognitive brain research, 25(3):668–677, 2005. [13] Shohei Tsuchimoto, Shuka Shibusawa, Seitaro Iwama, Masaaki Hayashi, Kohei Okuyama, Nobuaki Mizuguchi, Kenji Kato, and Junichi Ushiba. Use of common average reference and large-laplacian spatial-filters enhances eeg signal-to-noise ratios in intrinsic sensorimotor activity. Journal of neuroscience methods, 353:109089, 2021. [14] Wenzheng Qiu, Banghua Yang, Jun Ma, Shouwei Gao, Yan Zhu, and Wen Wang. The paradigm design of a novel 2-class unilateral upper limb motor imagery tasks and its eeg signal classification. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC), pages 152–155, 2021. [15] Mohammad Nur Alam, Muhammad Ibn Ibrahimy, and S. M. A. Motakabber. Feature extraction of eeg signal by power spectral density for motor imagery based bci. In 2021 8th International Conference on Computer and Communication Engineering (ICCCE), pages 234–237, 2021. [16] Gürol Canbek, Tugba Taskaya Temizel, and Seref Sagiroglu. Taskar: A research and education tool for calculation and representation of binary classification performance instruments. In 2021 International Conference on Information Security and Cryptology (ISCTURKEY), pages 105–110, 2021. [17] CF Blanco-Díaz, CD Guerrero-Méndez, and AF Ruiz-Olaya. Enhancing p300 detection using a band-selective filter bank for a visual p300 speller. IRBM, 44(3):100751, 2023. [18] Yaqi Chu, Xingang Zhao, Yijun Zou, Weiliang Xu, Guoli Song, Jianda Han, and Yiwen Zhao. Decoding multiclass motor imagery eeg from the same upper limb by combining riemannian geometry features and partial least squares regression. Journal of neural engineering, 17(4):046029, 2020. [19] Oluwarotimi Williams Samuel, Xiangxin Li, Yanjuan Geng, Pang Feng, Shixiong Chen, and Guanglin Li. Motor imagery classification of upper limb movements based on spectral domain features of eeg patterns. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 2976–2979, 2017. [20] Khin Pa Pa Aung and Khin Htar Nwe. Regions of interest (roi) analysis for upper limbs eeg neuroimaging schemes. In 2020 International Conference on Advanced Information Technologies (ICAIT), pages 53–58, 2020. [21] Shan Guan, Kai Zhao, Shuning Yang, et al. Motor imagery eeg classification based on decision tree framework and riemannian geometry. Computational intelligence and neuroscience, 2019, 2019. [22] Minsu Song and Jonghyun Kim. A paradigm to enhance motor imagery using rubber hand illusion induced by visuo-tactile stimulus. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(3):477–486, 2019. [23] Denis Delisle-Rodriguez, Leticia Silva, and Teodiano Bastos-Filho. Eeg changes during passive movements improve the motor imagery feature extraction in bcis-based sensory feedback calibration. Journal of Neural Engineering, 20(1):016047, 2023. [24] Aura X González-Cely, Cristian F Blanco-Díaz, Cristian D Guerrero-Mendez, and Teodiano F Bastos-Filho. Hand motor imagery identification using machine learning approaches in a protocol based on visual stimuli and passive movement. In 2023 IEEE Colombian Caribbean Conference (C3), pages 1–6. IEEE, 2023. [25] Juan A Barios, Santiago Ezquerro, Arturo Bertomeu-Motos, Marius Nann, Fco Javier Badesa, Eduardo Fernandez, Surjo R Soekadar, and Nicolas Garcia-Aracil. Synchronization of slow cortical rhythms during motor imagery-based brain–machine interface control. International journal of neural systems, 29(05):1850045, 2019. [26] Cristian F. Blanco-Díaz, Aura X. González-Cely, Cristian D. Guerrero-Mendez, Fernanda Souza, Diego Andrade, and Teodiano F. Bastos-Filho. Effects on cortical rhythms produced by robotic glove assistance during motor imagery. In 2023 IEEE Colombian Caribbean Conference (C3), pages 1–5, 2023. [27] Arpa Suwannarat, Setha Pan-Ngum, and Pasin Israsena. Comparison of eeg measurement of upper limb movement in motor imagery training system. Biomedical engineering online, 17(1):1–22, 2018. [28] Alexander A Frolov, Olesya Mokienko, Roman Lyukmanov, Elena Biryukova, Sergey Kotov, Lydia Turbina, Georgy Nadareyshvily, and Yulia Bushkova. Post-stroke rehabilitation training with a motor-imagery-based brain-computer interface (bci)-controlled hand exoskeleton: a randomized controlled multicenter trial. Frontiers in neuroscience, 11:400, 2017. [29] Cristian David Guerrero-Mendez, Cristian Felipe Blanco-Diaz, and Andres Felipe Ruiz-Olaya. How do factors of comfort, concentration, and eye fatigue affect the performance of a bci system based on ssvep? In 2021 IEEE 2nd International Congress of Biomedical Engineering and Bioengineering (CI-IB&BI), pages 1–4. IEEE, 2021. |
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Gonzalez Cely, Aura XimenaBlanco-Diaz, Cristian FelipeGuerrero Mendez, Cristian DavidVilla Parra, Ana CeciliaBastos-Filho, Teodiano Freire2024-12-24 00:00:002024-12-24 00:00:002024-12-24This study presents a novel strategy for classifying Motor Imagery (MI) related to hand opening/closing actions using electroencephalography signals. This approach combines the passive motion induced by a robotic glove and action observation. Two groups of subjects executed a protocol based on left and right hand movement MI to address this. Subsequently, spectral features were used on $mu$ and $beta$ bands, and machine-learning algorithms were used for classification. The results showed better performance for right-hand motion recognition using k-Nearest Neighbors (kNN), which achieved the highest performance metrics of 0.71, 0.76, and 0.28 for Accuracy (ACC), true positive rate, and false positive rate, respectively. These findings demonstrate the feasibility of the proposed methodology for improving the recognition of MI tasks of the same limb, which can contribute to the design of more robust brain-computer interfaces for the enhancement of rehabilitation therapy for post-stroke patients.application/pdfengUniversidad Tecnológica de BolívarAura Ximena Gonzalez Cely, Cristian Felipe Blanco-Diaz, Cristian David Guerrero Mendez, Ana Cecilia Villa Parra, Teodiano Freire Bastos-Filho - 2024https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.utb.edu.co/tesea/article/view/579MI-BCIUpper-limbClassificationMotor ImageryRobotic GloveClassification of opening/closing hand motor imagery induced by left and right robotic gloves through EEG signalsClassification of opening/closing hand motor imagery induced by left and right robotic gloves through EEG signalsArtículo de revistainfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Journal articleTextinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85https://doi.org/10.32397/tesea.vol5.n2.57910.32397/tesea.vol5.n2.5792745-0120Muhammad Ahmed Khan, Rig Das, Helle K. Iversen, and Sadasivan Puthusserypady. Review on motor imagery based bci systems for upper limb post-stroke neurorehabilitation: From designing to application. Computers in Biology and Medicine, 123:103843, 2020. [2] Tácia Cotinguiba Machado, Adriani Andrade Carregosa, Matheus S Santos, Nildo Manoel da Silva Ribeiro, and Ailton Melo. Efficacy of motor imagery additional to motor-based therapy in the recovery of motor function of the upper limb in post-stroke individuals: a systematic review. Topics in stroke rehabilitation, 26(7):548–553, 2019. [3] Bin Gu, Kun Wang, Long Chen, Jiatong He, Dingze Zhang, Minpeng Xu, Zhongpeng Wang, and Dong Ming. Study of the correlation between the motor ability of the individual upper limbs and motor imagery induced neural activities. Neuroscience, 530:56– 65, 2023. [4] Nicholas Cheng, Kok Soon Phua, Hwa Sen Lai, Pui Kit Tam, Ka Yin Tang, Kai Kei Cheng, Raye Chen-Hua Yeow, Kai Keng Ang, Cuntai Guan, and Jeong Hoon Lim. Brain-computer interface-based soft robotic glove rehabilitation for stroke. IEEE Transactions on Biomedical Engineering, 67(12):3339–3351, 2020. [5] Ning Guo, Xiaojun Wang, Dehao Duanmu, Xin Huang, Xiaodong Li, Yunli Fan, Hailan Li, Yongquan Liu, Eric Hiu Kwong Yeung, Michael Kai Tsun To, Jianxiong Gu, Feng Wan, and Yong Hu. Ssvep-based brain computer interface controlled soft robotic glove for post-stroke hand function rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30:1737–1744, 2022. [6] T.F. Bastos-Filho, A C Villa-Parra, C.D. Guerrero-Méndez, A X González-Cely, C F Blanco-Díaz, D. Delisle-Rodríguez, and T. Igasaki. A novel methodology based on static visual stimuli and kinesthetic motor imagery for upper limb neurorehabilitation. Research on Biomedical Engineering, page 1–14, 2024. [7] Ruyi Foong, Kai Keng Ang, Chai Quek, Cuntai Guan, Kok Soon Phua, Christopher Wee Keong Kuah, Vishwanath Arun Deshmukh, Lester Hon Lum Yam, Deshan Kumar Rajeswaran, Ning Tang, et al. Assessment of the efficacy of eeg-based mi-bci with visual feedback and eeg correlates of mental fatigue for upper-limb stroke rehabilitation. IEEE Transactions on Biomedical Engineering, 67(3):786–795, 2019. [8] Tadashi Yamamoto and Toyohiro Hamaguchi. Development of an application that implements a brain–computer interface to an upper-limb motor assistance robot to facilitate active exercise in patients: A feasibility study. Applied Sciences (Switzerland), 13(17), 2023. [9] Juan Sebastián Ramírez Archila and Alvaro David Orjuela-Cañón. Machine learning techniques for detecting motor imagery in upper limbs. In 2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020), pages 1–5, 2020. [10] Wing-Kin Tam, Kai-yu Tong, Fei Meng, and Shangkai Gao. A minimal set of electrodes for motor imagery bci to control an assistive device in chronic stroke subjects: A multi-session study. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(6):617–627, 2011. [11] Mahyar Tajdini, Volodymyr Sokolov, Ievgeniia Kuzminykh, Stavros Shiaeles, and Bogdan Ghita. Wireless sensors for brain activity—a survey. Electronics (Switzerland), 9(12):1– 26, 2020. [12] Christa Neuper, Reinhold Scherer, Miriam Reiner, and Gert Pfurtscheller. Imagery of motor actions: Differential effects of kinesthetic and visual–motor mode of imagery in single-trial eeg. Cognitive brain research, 25(3):668–677, 2005. [13] Shohei Tsuchimoto, Shuka Shibusawa, Seitaro Iwama, Masaaki Hayashi, Kohei Okuyama, Nobuaki Mizuguchi, Kenji Kato, and Junichi Ushiba. Use of common average reference and large-laplacian spatial-filters enhances eeg signal-to-noise ratios in intrinsic sensorimotor activity. Journal of neuroscience methods, 353:109089, 2021. [14] Wenzheng Qiu, Banghua Yang, Jun Ma, Shouwei Gao, Yan Zhu, and Wen Wang. The paradigm design of a novel 2-class unilateral upper limb motor imagery tasks and its eeg signal classification. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC), pages 152–155, 2021. [15] Mohammad Nur Alam, Muhammad Ibn Ibrahimy, and S. M. A. Motakabber. Feature extraction of eeg signal by power spectral density for motor imagery based bci. In 2021 8th International Conference on Computer and Communication Engineering (ICCCE), pages 234–237, 2021. [16] Gürol Canbek, Tugba Taskaya Temizel, and Seref Sagiroglu. Taskar: A research and education tool for calculation and representation of binary classification performance instruments. In 2021 International Conference on Information Security and Cryptology (ISCTURKEY), pages 105–110, 2021. [17] CF Blanco-Díaz, CD Guerrero-Méndez, and AF Ruiz-Olaya. Enhancing p300 detection using a band-selective filter bank for a visual p300 speller. IRBM, 44(3):100751, 2023. [18] Yaqi Chu, Xingang Zhao, Yijun Zou, Weiliang Xu, Guoli Song, Jianda Han, and Yiwen Zhao. Decoding multiclass motor imagery eeg from the same upper limb by combining riemannian geometry features and partial least squares regression. Journal of neural engineering, 17(4):046029, 2020. [19] Oluwarotimi Williams Samuel, Xiangxin Li, Yanjuan Geng, Pang Feng, Shixiong Chen, and Guanglin Li. Motor imagery classification of upper limb movements based on spectral domain features of eeg patterns. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 2976–2979, 2017. [20] Khin Pa Pa Aung and Khin Htar Nwe. Regions of interest (roi) analysis for upper limbs eeg neuroimaging schemes. In 2020 International Conference on Advanced Information Technologies (ICAIT), pages 53–58, 2020. [21] Shan Guan, Kai Zhao, Shuning Yang, et al. Motor imagery eeg classification based on decision tree framework and riemannian geometry. Computational intelligence and neuroscience, 2019, 2019. [22] Minsu Song and Jonghyun Kim. A paradigm to enhance motor imagery using rubber hand illusion induced by visuo-tactile stimulus. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(3):477–486, 2019. [23] Denis Delisle-Rodriguez, Leticia Silva, and Teodiano Bastos-Filho. Eeg changes during passive movements improve the motor imagery feature extraction in bcis-based sensory feedback calibration. Journal of Neural Engineering, 20(1):016047, 2023. [24] Aura X González-Cely, Cristian F Blanco-Díaz, Cristian D Guerrero-Mendez, and Teodiano F Bastos-Filho. Hand motor imagery identification using machine learning approaches in a protocol based on visual stimuli and passive movement. In 2023 IEEE Colombian Caribbean Conference (C3), pages 1–6. IEEE, 2023. [25] Juan A Barios, Santiago Ezquerro, Arturo Bertomeu-Motos, Marius Nann, Fco Javier Badesa, Eduardo Fernandez, Surjo R Soekadar, and Nicolas Garcia-Aracil. Synchronization of slow cortical rhythms during motor imagery-based brain–machine interface control. International journal of neural systems, 29(05):1850045, 2019. [26] Cristian F. Blanco-Díaz, Aura X. González-Cely, Cristian D. Guerrero-Mendez, Fernanda Souza, Diego Andrade, and Teodiano F. Bastos-Filho. Effects on cortical rhythms produced by robotic glove assistance during motor imagery. In 2023 IEEE Colombian Caribbean Conference (C3), pages 1–5, 2023. [27] Arpa Suwannarat, Setha Pan-Ngum, and Pasin Israsena. Comparison of eeg measurement of upper limb movement in motor imagery training system. Biomedical engineering online, 17(1):1–22, 2018. [28] Alexander A Frolov, Olesya Mokienko, Roman Lyukmanov, Elena Biryukova, Sergey Kotov, Lydia Turbina, Georgy Nadareyshvily, and Yulia Bushkova. Post-stroke rehabilitation training with a motor-imagery-based brain-computer interface (bci)-controlled hand exoskeleton: a randomized controlled multicenter trial. Frontiers in neuroscience, 11:400, 2017. [29] Cristian David Guerrero-Mendez, Cristian Felipe Blanco-Diaz, and Andres Felipe Ruiz-Olaya. How do factors of comfort, concentration, and eye fatigue affect the performance of a bci system based on ssvep? In 2021 IEEE 2nd International Congress of Biomedical Engineering and Bioengineering (CI-IB&BI), pages 1–4. IEEE, 2021.Transactions on Energy Systems and Engineering Applications519https://revistas.utb.edu.co/tesea/article/download/579/433Núm. 2 , Año 2024 : Transactions on Energy Systems and Engineering Applications220.500.12585/13534oai:repositorio.utb.edu.co:20.500.12585/135342025-09-16 09:15:13.325https://creativecommons.org/licenses/by/4.0Aura Ximena Gonzalez Cely, Cristian Felipe Blanco-Diaz, Cristian David Guerrero Mendez, Ana Cecilia Villa Parra, Teodiano Freire Bastos-Filho - 2024metadata.onlyhttps://repositorio.utb.edu.coRepositorio Digital Universidad Tecnológica de Bolívarbdigital@metabiblioteca.com |