Opsežna studija o upravljanju veštačkom rukom pomoću elektroencefalografije (EEG)

  • Ihab Abdulrahman Satam Univerzitet Obuda, Škola doktorskih bezbednosnih studija, Budimpešta, Mađarska; Severni tehnički univerzitet, Tehnički institut / Al-havija, Odeljenje za elektronske tehnike, Republika Irak https://orcid.org/0000-0002-9749-0944
Ključne reči: EEG, BCI, sveobuhvatna studija, prostetička ruka, kontroleri

Sažetak


Uvod/cilj: Signal u elektroencefalografiji (EEG) ima veliki uticaj na razvoj tehnologije upravljanja prostetičkom rukom. Pri funkcionalnom ispitivanju ljudskog pokreta kao glavno sredstvo koriste se EEG signali. Kontrola prostetičke ruke putem moždanih talasa je još u ranim fazama ispitivanja. Istraživači se tek od pre nekoliko godina bave ovom vrstom upravljanja.

Metode: Nekoliko studija je imalo za cilj da sistematično obradi do sada objavljena istraživanja kako bi se istraživačima i stručnjacima pružio sveobuhvatni pregled najnovijih tehnika upravljanja putem EEG signala koje  se koriste ne samo za prostetičke ruke već i za druge tehnologije. 

Rezultati: Upoređeno je 175 članaka, a izabrani su samo oni koji su najtešnje povezani sa studijom.

Zaključak: Ova studija ima tri cilja. Prvi je da skupi, sistematizuje i proceni informacije objavljene u studijama u periodu od 2011. do 2022. godine. Drugi je da pruži detaljniji izveštaj o holističkim, eksperimentalnim postignućima u ovoj oblasti, kao i o sadšnjim istraživanjima. Sistematično urađena studija obezbeđuje mnoštvo primera iz savremenih istraživanja upravljanja prostetičkom rukom putem EEG signala. Treći cilj jeste da se ukaže na oblasti koje zahtevaju dalja istraživanja, kao i da se preporuče pravci za njihovo sprovođenje.

Reference

Abdulrahman Satam, I. 2021. Review Studying of the Latest Development of Prosthetic Limbs Technologies. International Journal of Scientific & Engineering Research, 12(12), pp.721-731 [online]. Available at: https://www.ijser.org/research-paper-publishing-december-2021.aspx [Accessed: 20 November 2022].

Acharya, U.R., Hagiwara, Y., Deshpande, S.N., Suren, S., Koh, J.E.W., Oh, S.L., Arunkumar, N., Ciaccio, E.J. & Lim, C.M. 2019. Characterization of focal EEG signals: A review. Future Generation Computer Systems, 91, pp.290-299. Available at: https://doi.org/10.1016/j.future.2018.08.044>

Agashe, H.A., Paek, A.Y. & Contreras-Vidal, J.L. 2016. Chapter 4 - Multisession, noninvasive closed-loop neuroprosthetic control of grasping by upper limb amputees. Progress in Brain Research, 228, pp.107-128. Available at: https://doi.org/10.1016/bs.pbr.2016.04.016>

Ali, H.A., Goga, N., Vasilateanu, A., Ali, L.A., Abd-Almuhsen, G.S. & Naji, H.K. 2021. A Quantitative Research to Determine User’s Requirements for the Mind-Controlled Prosthesis Arm Intelligent System. In: 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Pitesti, Romania, pp.1-8, July 01-03. Available at: https://doi.org/10.1109/ECAI52376.2021.9515168>

Beyrouthy, T., Kork, S.A., Korbane, J.A. & Abouelela, M. 2017. EEG Mind Controlled Smart Prosthetic Arm – A Comprehensive Study. Advances in Science, Technology and Engineering Systems Journal, 2(3), pp.891-899. Available at: https://doi.org/10.25046/aj0203111>

Bhagat, N.A., Venkatakrishnan, A., Abibullaev, B., Artz, E.J., Yozbatiran, N., Blank, A.A., French, J., Karmonik, C., Grossman, R.G., O’Malley, M.K., Francisco, G.E. & Contreras-Vidal, J.L. 2016. Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors. Frontiers in Neuroscience, 10(March), art.number:122, pp.1-17. Available at: https://doi.org/10.3389/fnins.2016.00122>

Bhattacharyya, S., Konar, A. & Tibarewala, D.N. 2014. Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose. Medical & Biological Engineering & Computing, 52, pp.1007-1017. Available at: https://doi.org/10.1007/s11517-014-1204-4>

Bousseta, R., Ouakouak, I.El, Gharbi, M. & Regragui, F. 2018. EEG Based Brain Computer Interface for Controlling a Robot Arm Movement Through Thought. IRBM, 39(2), pp.129-135. Available at: https://doi.org/10.1016/j.irbm.2018.02.001>

Brauchle, D., Vukelić, M., Bauer, R. & Gharabaghi, A. 2015. Brain state-dependent robotic reaching movement with a multi-joint arm exoskeleton: Combining brain-machine interfacing and robotic rehabilitation. Frontiers in Human Neuroscience, 9(October), art.number:564, pp.1-13. Available at: https://doi.org/10.3389/fnhum.2015.00564>

Bridges, M.M., Para, M.P. & Mashner, M.J. 2011. Control system architecture for the modular prosthetic limb. Johns Hopkins Apl Technical Digest, 30(3), pp.217-222 [online]. Available at: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=aaefaff90b3f4102edb9c8de565a08ff1641deb5 [Accessed: 20 November 2022].

Bright, D., Nair, A., Salvekar, D. & Bhisikar, S. 2016. EEG-based brain controlled prosthetic arm. In: 2016 Conference on Advances in Signal Processing (CASP), Pune, India, pp.479-483, June 09-11. Available at: https://doi.org/10.1109/CASP.2016.7746219>

Buerkle, A., Eaton, W., Lohse, N., Bamber, T. & Ferreira, P. 2021. EEG based arm movement intention recognition towards enhanced safety in symbiotic Human-Robot Collaboration. Robotics and Computer-Integrated Manufacturing, 70(August), art.number:102137. Available at: https://doi.org/10.1016/j.rcim.2021.102137>

Chaudhry, A., Khan, U., Palla, M.R. & Singh, S.B. 2022. A Prosthetic Arm Based on Electroencephalography by Signal Acquisition and Processing on MATLAB. IJRESM - International Journal of Research in Engineering, Science and Management, 5(1), pp.119-124 [online]. Available at: https://journals.resaim.com/ijresm/article/view/1691 [Accessed: 20 November 2022].

Chinta, A., Mathur, M. & Lal, A.M. 2020. Mind Wave Controlled Prosthetic ARM Without using Brain Implants. International Journal of Recent Technology and Engineering (IJRTE), 8(5), pp.1615-1618. Available at: https://doi.org/10.35940/ijrte.e4801.018520>

Comani, S., Velluto, L., Schinaia, L., Cerroni, G., Serio, A., Buzzelli, S., Sorbi, S. & Guarnieri, B. 2015. Monitoring Neuro-Motor Recovery From Stroke With High-Resolution EEG, Robotics and Virtual Reality: A Proof of Concept. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(6), pp.1106-1116. Available at: https://doi.org/10.1109/TNSRE.2015.2425474>

Elnady, A.M., Zhang, X., Xiao, Z.G., Yong, X., Randhawa, B.K., Boyd, L. & Menon, C. 2015. A single-session preliminary evaluation of an affordable BCI-controlled arm exoskeleton and motor-proprioception platform. Frontiers in Human Neuroscience, 9(March), art.number:168, pp.1-14. Available at: https://doi.org/10.3389/fnhum.2015.00168>

Elstob, D. & Secco, E.L. 2016. A Low Cost Eeg Based BCI Prosthetic Using Motor Imagery. International Journal of Information Technology Convergence and Services (IJITCS), 6(1), pp.23-36. Available at: https://doi.org/10.5121/ijitcs.2016.6103>

Faiman, I., Pizzamiglio, S. & Turner, D.L. 2018. Resting-state functional connectivity predicts the ability to adapt arm reaching in a robot-mediated force field. Neuroimage, 174, pp.494-503. Available at: https://doi.org/10.1016/j.neuroimage.2018.03.054>

Ferdiansyah, F.A., Prajitno, P. & Wijaya, S.K. 2020. EEG-EMG based bio-robotics elbow orthotics control. Journal of Physics: Conference Series, 1528(art.number:012033), pp.1-6. Available at: https://doi.org/10.1088/1742-6596/1528/1/012033>

Formaggio, E., Storti, S.F., Galazzo, I.B., Gandolfi, M., Geroin, C., Smania, N., Spezia, L., Waldner, A., Fiaschi, A. & Manganotti, P. 2013. Modulation of event-related desynchronization in robot-assisted hand performance: brain oscillatory changes in active, passive and imagined movements. Journal of NeuroEngineering and Rehabilitation, 10(art.number:24). Available at: https://doi.org/10.1186/1743-0003-10-24>

Frolov, A.A., Mokienko, O., Lyukmanov, R., Biryukova, E., Kotov, S., Turbina, L., Nadareyshvily, G. & Bushkova, Y. 2017. 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(July), art.number:400, pp.1-11. Available at: https://doi.org/10.3389/fnins.2017.00400>

Fuentes-Gonzalez, J., Infante-Alarcon, A., Asanza, V. & Loayza, F.R. 2021. A 3D-Printed EEG based Prosthetic Arm. In: 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM), Shenzhen, China, pp.1-5, March, 01-02. Available at: https://doi.org/10.1109/HEALTHCOM49281.2021.9399035>

Gannouni, S., Belwafi, K., Aboalsamh, H., Alebdi, B., Almassad, Y., AlSamhan, Z. & Alobaedallah, H. 2020. EEG-Based BCI System to Control Prosthesis's Finger Movements (PREPRINT). Reacerch Square. Available at: https://doi.org/10.21203/rs.3.rs-49613/v1>

Ghani, F., Gaur, B., Varshney, S., Farooq, O. & Khan, Y.U. 2013. Detection of wrist movement using EEG signal for brain machine interface. In: 2013 International Conference on Technology, Informatics, Management, Engineering and Environment, Bandung, Indonesia, pp.5-8, June 23-26. Available at: https://doi.org/10.1109/TIME-E.2013.6611954>

Ghani, F., Jilani, M., Raghav, M., Farooq, O. & Khan, Y.U. 2012. Elbow movement detection using brain computer interface. In: 2012 8th International Conference on Computing Technology and Information Management (NCM and ICNIT), Seoul, Korea (South), pp.736-740, April 24-26 [online]. Available at: https://ieeexplore.ieee.org/document/6268597 [Accessed: 20 November 2022].

Gupta, S. & Singh, H. 1996. Preprocessing EEG signals for direct human-system interface. Proceedings IEEE International Joint Symposia on Intelligence and System, 1996, pp.32-37. Available at: https://doi.org/10.1109/ijsis.1996.565048>

Hortal, E., Planelles, D., Resquin, F., Climent, J.M., Azorín, J.M. & Pons, J.L. 2015. Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions. Journal of NeuroEngineering and Rehabilitation, 12(art.number:92). Available at: https://doi.org/10.1186/s12984-015-0082-9>

Huong, N.T.M., Linh, H.Q. & Khai, L.Q. 2018. Classification of left/right hand movement EEG signals using event related potentials and advanced features. In: Vo Van, T., Nguyen Le, T., Nguyen Duc, T. (Eds.) 6th International Conference on the Development of Biomedical Engineering in Vietnam (BME6). BME 2017. IFMBE Proceedings, 63, pp.209-215. Available at: https://doi.org/10.1007/978-981-10-4361-1_35>

Javed, A., Tiwana, M.I., Tiwana, M.I., Rashid, N., Iqbal, J. & Khan, U.S. 2017. Recognition of finger movements using EEG signals for control of upper limb prosthesis using logistic regression. Biomedical Research, 28(17), pp.7361-7369 [online]. Available at: https://www.biomedres.info/biomedical-research/recognition-of-finger-movements-using-eeg-signals-for-control-of-upper-limb-prosthesis-using-logistic-regression-8334.html [Accessed: 20 November 2022].

Jeong, J-H., Lee, B-H., Lee, D-H., Yun, Y-D. & Lee, S-W. 2020. EEG Classification of Forearm Movement Imagery Using a Hierarchical Flow Convolutional Neural Network. IEEE Access, 8, pp.66941-66950. Available at: https://doi.org/10.1109/ACCESS.2020.2983182>

Karakoc, A., Dogan, D. & Akinci, T.C. 2017. Robotic Arm Control Using The Brain Waves. The Journal of Cognitive Systems, 2(2), pp.51-54 [online]. Available at: https://dergipark.org.tr/en/pub/jcs/issue/42261/530481 [Accessed: 20 November 2022].

Ketenci, S. & Kayikcioglu, T. 2019. Investigation of Theta Rhythm Effect in Detection of Finger Movement. Journal of Experimental Neuroscience, 13, pp.1-5. Available at: https://doi.org/10.1177/1179069519828737>

Krichner, E.A., Tabie, M. & Seeland, A. 2014. Multimodal Movement Prediction - Towards an Individual Assistance of Patients. PLOS ONE, 9(1), art.ID:e85060, pp.1-9. Available at: https://doi.org/10.1371/journal.pone.0085060>

Li, T., Xue, T., Wang, B. & Zhang, J. 2018. Decoding Voluntary Movement of Single Hand Based on Analysis of Brain Connectivity by Using EEG Signals. Frontiers in Human Neuroscience, 12(November), art.number:381, pp.1-14. Available at: https://doi.org/10.3389/fnhum.2018.00381>

Liao, K., Xiao, R., Gonzalez, J. & Ding, L. 2014. Decoding Individual Finger Movements from One Hand Using Human EEG Signals. PLOS ONE, 9(1), art.number: e85192, pp.1-12. Available at: https://doi.org/10.1371/journal.pone.0085192>

Looned, R., Webb, J.,  Xiao, Z.G. & Menon, C. 2014. Assisting drinking with an affordable BCI-controlled wearable robot and electrical stimulation: a preliminary investigation. Journal of NeuroEngineering and Rehabilitation, 11(art.number:51]. Available at: https://doi.org/10.1186/1743-0003-11-51>

Mandekar, S., Holland, A., Thielen, M., Behbahani, M. & Melnykowycz, M. 2022. Advancing towards Ubiquitous EEG, Correlation of In-Ear EEG with Forehead EEG. Sensors, 22(4), art.number:1568. Available at: https://doi.org/10.3390/s22041568>

Meng, J., Zhang, S., Bekyo, A., Olsoe, J., Baxter, B. & He, B. 2016. Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks.  Scientific Reports, 6(art.number: 38565), pp.1-15. Available at: https://doi.org/10.1038/srep38565>

Mohamed, A-K. & Aharonson, V. 2021. Four-class BCI discrimination of right and left wrist and finger movements. IFAC-PapersOnLine, 54(21), pp.91-96. Available at: https://doi.org/10.1016/j.ifacol.2021.12.016>

Murphy, D.P., Bai, O., Gorgey, A.S., Fox, J., Lovegreen, W.T., Burkhardt, B.W., Atri, R., Marquez, J.S., Li, Q. & Fei, D-Y. 2017. Electroencephalogram-Based Brain–Computer Interface and Lower-Limb Prosthesis Control: A Case Study. Frontiers in Neurology, 8(December), art.number:696, pp.1-8. Available at: https://doi.org/10.3389/fneur.2017.00696>

Noel, T.C. & Snider, B.R. 2019. Utilizing Deep Neural Networks for Brain: Computer Interface-Based Prosthesis Control. Journal of Computing Sciences in Colleges, 35(1), pp.93-101 [online]. Available at: http://ccsc.org/publications/journals/NW2019.pdf [Accessed: 20 November 2022].

Osama, M. & Allauddin, U. 2022. Design and modelling of lower prosthetic limb for additive manufacturing. In: IMEC-2022: 11th International Mechanical Engineering Conference, Karachi, Pakistan, p.27, January 14-15 [online]. Available at: https://imec.neduet.edu.pk/sites/default/files/IMEC_Proceedings_2022.pdf [Accessed: 20 November 2022].

Paek, A.Y., Agashe, H.A. & Contreras-Vidal, J.L. 2014. Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography. Frontiers in Neuroengineering, 7(March), art.number:3, pp.1-18. Available at: https://doi.org/10.3389/fneng.2014.00003>

Ramalingam, V.V., Pandian, A. & Parivel, R. 2016. Controlling Artificial Limb Movement System using EEG Signals. Indian Journal of Science and Technology, 9(47), pp.1-7. Available at: https://doi.org/10.17485/ijst/2016/v9i47/107945>

Ramos-Murguialday, A., Schürholz, M., Caggiano, V., Wildgruber, M., Caria, A., Hammer, E.M., Halder, S. & Birbaumer, N. 2012. Proprioceptive Feedback and Brain Computer Interface (BCI) Based Neuroprostheses. PLOS ONE, 7(10), art.ID: e47048, pp.1-10. Available at: https://doi.org/10.1371/journal.pone.0047048>

Rashid, N., Iqbal, J., Javed, A., Tiwana, M.I. & Khan, U.S. 2018. Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis. BioMed Research International, 2018(art.ID:2695106). Available at: https://doi.org/10.1155/2018/2695106>

Setiawan, J.D., Alwy, F., Ariyanto, M., Samudro, L. & Ismail, R. 2021. Flexion and Extension Motion for Prosthetic Hand Controlled by Single-Channel EEG. In: 2021 8th International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE), Semarang, Indonesia, pp.47-52, September 23-24. Available at: https://doi.org/10.1109/ICITACEE53184.2021.9617220>

Shedeed, H.A., Issa, M.F. & El-Sayed, S.M. 2013. Brain EEG signal processing for controlling a robotic arm. In: 2013 8th International Conference on Computer Engineering & Systems (ICCES), Cairo, Egypt, pp.152-157, November 26-28. Available at: https://doi.org/10.1109/ICCES.2013.6707191>

Soekadar, S.R., Witkowski M., Vitiello, N. & Birbaumer, N. 2015. An EEG/EOG-based hybrid brain-neural computer interaction (BNCI) system to control an exoskeleton for the paralyzed hand Biomedical Engineering / Biomedizinische Technik, 60(3), pp.199-205. Available at: https://doi.org/10.1515/bmt-2014-0126>

Steinisch, M., Tana, M.G. & Comani, S. 2013. A Post-Stroke Rehabilitation System Integrating Robotics, VR and High-Resolution EEG Imaging. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(5), pp.849-859. Available at: https://doi.org/10.1109/TNSRE.2013.2267851>

Szabolcsi, R. 2019. Modern Control Engineering. Budapest: Óbuda University. ISBN: 978-963-449-1880.

Szabolcsi, R. 2020. Control System Design Using MATLAB. Budapest: Óbuda University. ISBN: 978-963-449-1873.

Tang, Z., Sun, S., Zhang, S., Chen, Y., Li, C. & Chen, S. 2016. A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control. Sensors, 16(2), art.number:2050. Available at: https://doi.org/10.3390/s16122050>

Tung, S.W., Guan, C., Ang, K.K., Phua, K.S., Wang, C., Zhao, L., Teo, W.P. & Chew, E. 2013. Motor imagery BCI for upper limb stroke rehabilitation: An evaluation of the EEG recordings using coherence analysis. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, pp.261-264, July 03-07. Available at: https://doi.org/10.1109/EMBC.2013.6609487>

Übeyli, E.D. 2009. Combined neural network model employing wavelet coefficients for EEG signals classification. Digital Signal Processing, 19(2), pp.297-308. Available at: https://doi.org/10.1016/j.dsp.2008.07.004>

Vidaurre, C., Klauer, C., Schauer, T., Ramos-Murguialday, A. & Müller, K-R. 2016. EEG-based BCI for the linear control of an upper-limb neuroprosthesis. Medical Engineering & Physics, 38(11), pp.1195-1204. Available at: https://doi.org/10.1016/j.medengphy.2016.06.010>

Wen, T., Du, Y., Pan, T., Huang, C. & Zhang, Z. 2021. A Deep Learning-Based Classification Method for Different Frequency EEG Data. Computational and Mathematical Methods in Medicine, 2021(art.ID:1972662). Available at: https://doi.org/10.1155/2021/1972662>

Witkowski, M., Cortese, M., Cempini, M., Mellinger, J., Vitiello, N. & Soekadar, S.R. 2014. Enhancing brain-machine interface (BMI) control of a hand exoskeleton using electrooculography (EOG). Journal of NeuroEngineering and Rehabilitation, 11(art.number:165). Available at: https://doi.org/10.1186/1743-0003-11-165>

Xu, B., Peng, S., Song, A., Yang, R. & Pan, L. 2011. Robot-Aided Upper-Limb Rehabilitation Based on Motor Imagery EEG. International Journal of Advanced Robotic Systems, 8(4). Available at: https://doi.org/10.5772/45703>

Xu, B., Song, A., Zhao, G., Xu, G., Pan, L., Yang, R., Li, H. & Cui, J. 2015. Design and evaluation of a motor imagery electroencephalogram-controlled robot system. Advances in Mechanical Engineering, 7(3). Available at: https://doi.org/10.1177/1687814015573607>

Yanagisawa, T., Hirata, M., Saitoh, Y., Goto, T., Kishima, H., Fukuma, R., Yokoi, H., Kamitani, Y. & Yoshimine, T. 2011. Real-time control of a prosthetic hand using human electrocorticography signals: Technical note. JNS – Journal of Neurosurgery, 114(6), pp.1715-1722. Available at: https://doi.org/10.3171/2011.1.JNS101421>

Zhang, A., Yang, B. & Huang, L. 2008. Feature extraction of EEG signals using power spectral entropy. In: 2008 International Conference on BioMedical Engineering and Informatics, Sanya, China, pp.435-439, May 27-30. Available at: https://doi.org/10.1109/BMEI.2008.254>

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2023/01/30
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Originalni naučni radovi