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Concept of using the brain-computer interface to control hand prosthesis

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EN
Abstrakty
EN
This study examines the possibility of implementing intelligent artificial limbs for patients after injuries or amputations. Brain-computer technology allows signals to be acquired and sent between the brain and an external device. Upper limb prostheses, however, are quite a complicated tool, because the hand itself has a very complex structure and consists of several joints. The most complicated joint is undoubtedly the saddle joint, which is located at the base of the thumb. You need to demonstrate adequate anatomical knowledge to construct a prosthesis that will be easy to use and resemble a human hand as much as possible. It is also important to create the right control system with the right software that will easily work together with the brain-computer interface. Therefore, the proposed solution in this work consists of three parts, which are: the Emotiv EPOC + Neuroheadsets, a control system made of a servo and an Arduino UNO board (with dedicated software), and a hand prosthesis model made in the three-dimensional graphic program Blender and printed using a 3D printer. Such a hand prosthesis controlled by a signal from the brain could help people with disabilities after amputations and people who have damaged innervation at the stump site.
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autor
  • Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, 45-758, Poland
  • Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, 45-758, Poland
Bibliografia
  • [1] Ramadan, R.A.; Vasilakos, A. V. Brain computer interface: control signals review. Neurocomputing, vol. 223, 2017, 26–44, doi:10.1016/J.NEUCOM.2016.10.024.
  • [2] Bernal, S.L.; Celdrán, A.H.; Pérez, G.M. Neuronal Jamming cyberattack over invasive BCIs affecting the resolution of tasks requiring visual capabilities. Comput. Secur. vol. 112, 2022, doi:10.1016/J.COSE.2021.102534.
  • [3] Shivwanshi, R.R.; Nirala, N. Concept of AI for acquisition and modeling of noninvasive modalities for BCI. Artif. Intell. Brain-Computer Interface, 2022, 121–144, doi:10.1016/B978-0-323-91197-9.00007-2.
  • [4] Dagdevir, E.; Tokmakci, M. Optimization of preprocessing stage in EEG based BCI systems in terms of accuracy and timing cost. Biomed. Signal Process. Control, 2021, 67, doi:10.1016/j.bspc.2021.102548.
  • [5] Bassi, P.R.A.S.; Rampazzo, W.; Attux, R. Transfer learning and SpecAugment applied to SSVEP based BCI classification. arXiv 2020, doi:10.1016/j.bspc.2021.102542.
  • [6] Vilela, M.; Hochberg, L.R. Applications of brain-computer interfaces to the control of robotic and prosthetic arms. In Handbook of Clinical Neurology; Elsevier B.V., 2020; vol. 168, pp. 87–99.
  • [7] Na, R.; Hu, C.; Sun, Y.; Wang, S.; Zhang, S.; Han, M.; Yin, W.; Zhang, J.; Chen, X.; Zheng, D. An embedded lightweight SSVEP-BCI electric wheelchair with hybrid stimulator. Digit. Signal Process. vol. 116, 2021, 103101, doi:10.1016/J.DSP.2021.103101.
  • [8] Robinson, N.; Mane, R.; Chouhan, T.; Guan, C. Emerging trends in BCI-robotics for motor control and rehabilitation. Curr. Opin. Biomed. Eng. vol. 20, 2021, 100354, doi:10.1016/J.COBME.2021.100354.
  • [9] Miladinović, A.; Ajčević, M.; Jarmolowska, J.; Marusic, U.; Colussi, M.; Silveri, G.; Battaglini, P.P.; Accardo, A. Effect of power feature covariance shift on BCI spatial-filtering techniques: A comparative study. Comput. Methods Programs Biomed. vol. 198, 2021, doi:10.1016/j.cmpb.2020.105808.
  • [10] Soman, S.; Murthy, B.K. Using Brain Computer Interface for synthesized speech communication for the physically disabled. In Proceedings of the Procedia Computer Science; Elsevier B.V., vol. 46, 2015; 292–298.
  • [11] Noori, F.M.; Naseer, N.; Qureshi, N.K.; Nazeer, H.; Khan, R.A. Optimal feature selection from fNIRS signals using genetic algorithms for BCI. Neurosci. Lett. vol. 647, 2017, 61–66, doi:10.1016/j.neulet.2017.03.013.
  • [12] Gubert, P.H.; Costa, M.H.; Silva, C.D.; Trofino-Neto, A. The performance impact of data augmentation in CSP-based motor-imagery systems for BCI applications. Biomed. Signal Process. Control, vol. 62, 2020, doi:10.1016/j.bspc.2020.102152.
  • [13] Hernández-Del-Toro, T.; Reyes-García, C.A.; Villaseñor-Pineda, L. Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signals. Biomed. Signal Process. Control, vol. 65, 2021, doi:10.1016/j.bspc.2020.102351.
  • [14] Shi, B.; Wang, Q.; Yin, S.; Yue, Z.; Huai, Y.; Wang, J. A binary harmony search algorithm as channel selection method for motor imagery-based BCI. Neurocomputing, vol. 443, 2021, 12–25, doi:10.1016/j.neucom.2021.02.051.
  • [15] Janani A.; Sasikala M.; Chhabra, H.; Shajil, N.; Venkatasubramanian, G. Investigation of deep convolutional neural network for classification of motor imagery fNIRS signals for BCI applications. Biomed. Signal Process. Control, vol. 62, 2020, 102133, doi:10.1016/j.bspc.2020.102133.
  • [16] Zarrintaj, P.; Saeb, M.R.; Ramakrishna, S.; Mozafari, M. Biomaterials selection for neuroprosthetics. Curr. Opin. Biomed. Eng., vol. 6, 2018, 99–109.
  • [17] Kasim, M.A.A.; Low, C.Y.; Ayub, M.A.; Zakaria, N.A.C.; Salleh, M.H.M.; Johar, K.; Hamli, H. User-Friendly LabVIEW GUI for Prosthetic Hand Control Using Emotiv EEG Headset. In Proceedings of the Procedia Computer Science; Elsevier B.V., vol. 105, 2017; 276–281.
  • [18] Lange, G.; Low, C.Y.; Johar, K.; Hanapiah, F.A.; Kamaruzaman, F. Classification of Electroencephalogram Data from Hand Grasp and Release Movements for BCI Controlled Prosthesis. Procedia Technol., vol. 26, 2016, 374–381, doi:10.1016/j.protcy.2016.08.048.
  • [19] Alazrai, R.; Alwanni, H.; Daoud, M.I. EEG-based BCI system for decoding finger movements within the same hand. Neurosci. Lett., vol. 698, 2019, 113–120, doi:10.1016/j.neulet.2018.12.045.
  • [20] Downey, J.E.; Brooks, J.; Bensmaia, S.J. Artificial sensory feedback for bionic hands. In Intelligent Biomechatronics in Neurorehabilitation; Elsevier, 2019; pp. 131–145 ISBN 9780128149423.
  • [21] Guger, C.; Harkam, W.; Hertnaes, C.; Pfurtscheller, G. Prosthetic Control by an EEG-based BrainComputer Interface (BCI).
  • [22] Müller-Putz, G.R.; Pfurtscheller, G. Control of an electrical prosthesis with an SSVEP-based BCI. IEEE Trans. Biomed. Eng., vol. 55, 2008, 361–364, doi:10.1109/TBME.2007.897815.
  • [23] Beyrouthy, T.; Al Kork, S.K.; Korbane, J.A.; Abdulmonem, A. EEG Mind controlled Smart Prosthetic Arm. In Proceedings of the 2016 IEEE International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societies (EmergiTech); IEEE, 2016; pp. 404–409.
  • [24] Constantine, A.; Asanza, V.; Loayza, F.R.; Peláez, E.; Peluffo-Ordóñez, D. BCI System using a Novel Processing Technique Based on Electrodes Selection for Hand Prosthesis Control. IFAC-PapersOnLine, vol. 54, 2021, 364–369, doi:10.1016/J.IFACOL.2021.10.283.
  • [25] Sensinger, J.W.; Hill, W.; Sybring, M. Prostheses—Assistive Technology—Upper. Encycl. Biomed. Eng. vols. 2-3, 2019, 632–644, doi:10.1016/B978-0-12-801238-3.99912-4.
  • [26] EMOTIV Website online: www.emotiv.com (accessed on August 2022)
  • [27] ARDUINO Website online: https://store.arduino.cc/ (accessed on August 2022)
  • [28] BLENDER Website online: www.blender.org (accessed on August 2022)
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5e5dba25-7acc-4a4f-8176-0cd313b59658
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