Enhancing cortical rhythms through robotic glove-assisted motor imagery training

Cristian Felipe Blanco-Díaz; Aura Ximena González-Cely; Cristian David Guerrero-Mendez; Ana Cecilia Villa-Parra; Teodiano Bastos-Filho

doi:10.32397/tesea.vol6.n2.580

Authors

Cristian Felipe Blanco-Díaz Federal University of Espírito Santo https://orcid.org/0000-0002-8083-7454
Aura Ximena González-Cely Federal University of Espírito Santo https://orcid.org/0000-0001-9304-0834
Cristian David Guerrero-Mendez Federal University of Espírito Santo https://orcid.org/0000-0003-1019-9423
Ana Cecilia Villa-Parra Politecnica Salesiana University
Teodiano Bastos-Filho Federal University of Espírito Santo https://orcid.org/0000-0002-1185-2773

DOI:

https://doi.org/10.32397/tesea.vol6.n2.580

Keywords:

EEG, Hand Motor Imagery, Robotic Glove, Upper-Limb Rehabilitation, Action Observation

Abstract

Improving Motor Imagery (MI) results in amplified event-related Desynchronization/Synchoronization (ERD/ERS), which is crucial for MI-based Brain-Computer Interface (BCIs) and neuromotor rehabilitation. Several attempts to enhance MI by providing visual guidance have been reported, however, this can be improved by using robotic systems for Kinesthetic MI (KMI), such as robotic gloves for hand movements. In this study, a novel protocol based on KMI tasks is proposed to analyze cortical rhythms against a standard MI protocol based on static Action Observation (AO). The results allow concluding that the use of the robotic gloves increases the ERDs by 3% during MI tasks, where neuromodulations were found to be more focused on the C₃ and C₄ electrodes during right- and left-hand closing MI, respectively, compared to opening. In conclusion, the proposed strategy based on passive movement is feasible and can enhance MI, which can be used for designing most robust BCIs.

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References

Ramadhan Rashid Said, Md Belal Bin Heyat, Keer Song, Chao Tian, and Zhe Wu. A systematic review of virtual reality and robot therapy as recent rehabilitation technologies using eeg-brain–computer interface based on movement-related cortical potentials. Biosensors, 12(12):1134, 2022.

Mostafa Orban, Mahmoud Elsamanty, Kai Guo, Senhao Zhang, and Hongbo Yang. A review of brain activity and eeg-based brain–computer interfaces for rehabilitation application. Bioengineering, 9(12):768, 2022.

Jonathan R Wolpaw, José del R Millán, and Nick F Ramsey. Brain-computer interfaces: Definitions and principles. Handbook of clinical neurology, 168:15–23, 2020.

Annalisa Colucci, Mareike Vermehren, Alessia Cavallo, Cornelius Angerhöfer, Niels Peekhaus, Loredana Zollo, Won-Seok Kim, Nam-JongPaik, and Surjo RSoekadar. Brain–computer interface-controlled exoskeletons in clinical neurorehabilitation: ready or not? Neurorehabilitation and Neural Repair, 36(12):747–756, 2022.

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.

Long Chen, Bin Gu, Zhongpeng Wang, Lei Zhang, Minpeng Xu, Shuang Liu, Feng He, and Dong Ming. Eeg-controlled functional electrical stimulation rehabilitation for chronic stroke: system design and clinical application. Frontiers of medicine, 15(5):740–749, 2021.

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.

Ning Guo, Xiaojun Wang, Dehao Duanmu, Xin Huang, Xiaodong Li, Yunli Fan, Hailan Li, Yongquan Liu, Eric Hiu Kwong Yeung, Michael Kai Tsun To, et al. 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.

T. Bastos-Filho, A.C. Villa-Parra, C.D. Guerrero-Mendez, A.X. González-Cely, C.F. Blanco-Díaz, D. Delisle-Rodriguez, and T. Igasaki. A novel methodology based on static visual stimuli and kinesthetic motor imagery for upper limb neurorehabilitation (submitted). Research on Biomedical Engineering, 2024.

Margaret C. Thompson. Critiquing the concept of BCI illiteracy. Science and Engineering Ethics, 25(4):1217–1233, August 2018.

Reza Abiri, Soheil Borhani, Eric W Sellers, Yang Jiang, and Xiaopeng Zhao. A comprehensive review of eeg-based brain–computer interface paradigms. Journal of Neural Engineering, 16(1):011001, jan 2019.

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.

Camille Jeunet, Bertrand Glize, Aileen McGonigal, Jean-Marie Batail, and Jean-Arthur Micoulaud-Franchi. Using eeg-based brain computer interface and neurofeedback targeting sensorimotor rhythms to improve motor skills: Theoretical background, applications and prospects. Neurophysiologie Clinique, 49(2):125–136, 2019.

Dennis J McFarland, Laurie A Miner, Theresa M Vaughan, and Jonathan R Wolpaw. Mu and beta rhythm topographies during motor imagery and actual movements. Brain topography, 12:177–186, 2000.

Natasha Padfield, Jaime Zabalza, Huimin Zhao, Valentin Masero, and Jinchang Ren. Eeg-based brain-computer interfaces using motor-imagery: Techniques and challenges. Sensors, 19(6):1423, 2019.

Cristian D Guerrero-Mendez, Cristian F Blanco-Diaz, Hamilton Rivera-Flor, Alberto F De Souza, Sebastian Jaramillo-Isaza, Andres F Ruiz-Olaya, and Teodiano F Bastos-Filho. Coupling effects of cross-corticomuscular association during object manipulation tasks on different haptic sensations. NeuroSci, 4(3):195–210, 2023.

Dariusz Zapała, Paulina Iwanowicz, Piotr Francuz, and Paweł Augustynowicz. Handedness effects on motor imagery during kinesthetic and visual-motor conditions. Scientific reports, 11(1):13112, 2021.

Rami Alazrai, Motaz Abuhijleh, Hisham Alwanni, and Mohammad I Daoud. A deep learning framework for decoding motor imagery tasks of the same hand using eeg signals. IEEE Access, 7:109612–109627, 2019.

Mojgan Tavakolan, Zack Frehlick, Xinyi Yong, and Carlo Menon. Classifying three imaginary states of the same upper extremity using time-domain features. PloS one, 12(3):e0174161, 2017.

Cristian D. Guerrero-Mendez, Cristian F. Blanco-Diaz, Denis Delisle-Rodriguez, Andrés F. Ruiz-Olaya, Sebastián Jaramillo-Isaza, and Teodiano F. Bastos-Filho. Analysis of eeg rhythms during four-direction first-person reach-to-grasp kinesthetic motor imagery tasks from the same limb. In 2023 IEEE 3rd Colombian BioCAS Workshop, pages 1–6, 2023.

Woo Hyung Lee, Eunkyung Kim, Han Gil Seo, Byung-Mo Oh, Hyung Seok Nam, Yoon Jae Kim, Hyun Haeng Lee, Min-Gu Kang, Sungwan Kim, and Moon Suk Bang. Target-oriented motor imagery for grasping action: different characteristics of brain activation between kinesthetic and visual imagery. Scientific reports, 9(1):1–14, 2019.

Daniele Esposito, Jessica Centracchio, Emilio Andreozzi, Sergio Savino, Gaetano D Gargiulo, Ganesh R Naik, and Paolo Bifulco. Design of a 3d-printed hand exoskeleton based on force-myography control for assistance and rehabilitation. Machines, 10(1):57, 2022.

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.

Francesco Missiroli, Michele Barsotti, Daniele Leonardis, Massimiliano Gabardi, Giulio Rosati, and Antonio Frisoli. Haptic stimulation for improving training of a motor imagery bci developed for a hand-exoskeleton in rehabilitation. In 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), pages 1127–1132. IEEE, 2019.

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, August 2020.

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.

Takuro Zama, Yoshiyuki Takahashi, and Sotaro Shimada. Simultaneous eeg-nirs measurement of the inferior parietal lobule during a reaching task with delayed visual feedback. Frontiers in human neuroscience, 13:301, 2019.

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.

Michele Barsotti, D Leonardis, C Loconsole, Massimiliano Solazzi, E Sotgiu, C Procopio, C Chisari, M Bergamasco, and AFrisoli. A full upper limb robotic exoskeleton for reaching and grasping rehabilitation triggered by mi-bci. In 2015 IEEE international conference on rehabilitation robotics (ICORR), pages 49–54. IEEE, 2015.