1. Kim JS, Caplan LR. Clinical stroke syndromes. Intracranial Atherosclerosis: Pathophysiology, Diagnosis and Treatment. 2016; 40: 72-92. [ DOI:10.1159/000448303] 2. Cheung VC, Piron L, Agostini M, Silvoni S, Turolla A, Bizzi E. Stability of muscle synergies for voluntary actions after cortical stroke in humans. Proceedings of the National Academy of Sciences. 2009; 106(46): 19563-8. [ DOI:10.1073/pnas.0910114106] 3. Krakauer JW. Motor learning: its relevance to stroke recovery and neurorehabilitation. Current opinion in neurology. 2006; 19(1): 84-90. [ DOI:10.1097/01.wco.0000200544.29915.cc] 4. Babaiasl M, Mahdioun SH, Jaryani P, Yazdani M. A review of technological and clinical aspects of robot-aided rehabilitation of upper-extremity after stroke. Disability and Rehabilitation: Assistive Technology. 2016; 11(4): 263-80. [ DOI:10.3109/17483107.2014.1002539] 5. Hara Y. Neurorehabilitation with new functional electrical stimulation for hemiparetic upper extremity in stroke patients. Journal of Nippon Medical School. 2008; 75(1): 4-14. [ DOI:10.1272/jnms.75.4] 6. Bonini-Rocha AC, de Andrade ALS, dos Santos Pereira R, de Moraes AM, Matheus LBG, da Fonseca ST, et al. Biofeedback interventions for short term upper limb function following stroke: A systematic review with meta-analysis. Journal of Hand Therapy. 2022. [ DOI:10.1016/j.jht.2022.05.001] 7. Schmidt RA, White JL. Evidence for an error detection mechanism in motor skills: A test of Adams' closed-loop theory. Journal of Motor Behavior. 1972; 4(3): 143-53. [ DOI:10.1080/00222895.1972.10734930] 8. Seitz AR. Cognitive neuroscience: targeting neuroplasticity with neural decoding and biofeedback. Current Biology. 2013; 23(5): R210-R2. [ DOI:10.1016/j.cub.2013.01.015] 9. Huang H, Wolf SL, He J. Recent developments in biofeedback for neuromotor rehabilitation. Journal of neuroengineering and rehabilitation. 2006; 3(1): 1-12. [ DOI:10.1186/1743-0003-3-11] 10. Teasell R, McIntyre A, Viana R, Bateman EA, Murie-Fernandez M, Janzen S, et al. Developing a framework for utilizing adjunct rehabilitation therapies in motor recovery of upper extremity post stroke. Topics in Stroke Rehabilitation. 2022: 1-8. [ DOI:10.1080/10749357.2022.2070364] 11. Hall S, Wild F. Real-time auditory biofeedback system for learning a novel arm trajectory: a usability study. Perspectives on Wearable Enhanced Learning (WELL): Springer; 2019. p. 385-409. [ DOI:10.1007/978-3-319-64301-4_18] 12. Isakova E, Egorova YV. Visual and acoustic feedback on the support reaction for upper and lower extremities: a case study of a female patient after a stroke. Almanac of Clinical Medicine. 2021; 49(6): 435-42. [ DOI:10.18786/2072-0505-2021-49-016] 13. Hankinson K, Shaykevich A, Vallence A-M, Rodger J, Rosenberg M, Etherton-Beer C. A Tailored Music-Motor Therapy and Real-Time Biofeedback Mobile Phone App ('GotRhythm') to Promote Rehabilitation Following Stroke: A Pilot Study. Neuroscience Insights. 2022; 17: 26331055221100587. [ DOI:10.1177/26331055221100587] 14. Giorgino T, Tormene P, Maggioni G, Capozzi D, Quaglini S, Pistarini C. Assessment of sensorized garments as a flexible support to self-administered post-stroke physical rehabilitation. European Journal of Physical and Rehabilitation Medicine. 2009; 45(1): 75-84. 15. Kim C-Y, Lee J-S, Lee J-H, Kim Y-G, Shin A-R, Shim Y-H, et al. Effect of spatial target reaching training based on visual biofeedback on the upper extremity function of hemiplegic stroke patients. Journal of physical therapy science. 2015; 27(4): 1091-6. [ DOI:10.1589/jpts.27.1091] 16. Brokaw EB, Eckel E, Brewer BR. Usability evaluation of a kinematics focused Kinect therapy program for individuals with stroke. Technology and Health Care. 2015; 23(2): 143-51. [ DOI:10.3233/THC-140880] 17. Lin L-F, Lin Y-J, Lin Z-H, Chuang L-Y, Hsu W-C, Lin Y-H. Feasibility and efficacy of wearable devices for upper limb rehabilitation in patients with chronic stroke: a randomized controlled pilot study. European journal of physical and rehabilitation medicine. 2017; 54(3): 388-96. [ DOI:10.23736/S1973-9087.17.04691-3] 18. Ögün MN, Kurul R, Yaşar MF, Turkoglu SA, Avci Ş, Yildiz N. Effect of leap motion-based 3D immersive virtual reality usage on upper extremity function in ischemic stroke patients. Arquivos de neuro-psiquiatria. 2019; 77: 681-8. [ DOI:10.1590/0004-282x20190129] 19. Kobravi H-R, Erfanian A. Decentralized adaptive robust control based on sliding mode and nonlinear compensator for the control of ankle movement using functional electrical stimulation of agonist-antagonist muscles. Journal of neural engineering. 2009; 6(4): 046007. [ DOI:10.1088/1741-2560/6/4/046007] 20. Zadnia A, Kobravi HR, Sheikh M, Hosseini HA. Generating the visual biofeedback signals applicable to reduction of wrist spasticity: A pilot study on stroke patients. Basic and Clinical Neuroscience. 2018; 9(1): 15. [ DOI:10.29252/nirp.bcn.9.1.15] 21. Preston DC, Shapiro BE. Electromyography and neuromuscular disorders e-book: clinical-electrophysiologic correlations (Expert Consult-Online): Elsevier Health Sciences; 2012. [ DOI:10.1016/B978-1-4557-2672-1.00016-7] 22. Merletti R, Aventaggiato M, Botter A, Holobar A, Marateb H, Vieira TM. Advances in surface EMG: recent progress in detection and processing techniques. Critical Reviews™ in Biomedical Engineering. 2010; 38(4). [ DOI:10.1615/CritRevBiomedEng.v38.i4.10] 23. Merletti R, Di Torino P. Standards for reporting EMG data. J Electromyogr Kinesiol. 1999; 9(1): 3-4. 24. Clark DJ, Ting LH, Zajac FE, Neptune RR, Kautz SA. Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke. Journal of neurophysiology. 2010; 103(2): 844-57. [ DOI:10.1152/jn.00825.2009] 25. Zhang F, Bazarevsky V, Vakunov A, Tkachenka A, Sung G, Chang C-L, et al. Mediapipe hands: On-device real-time hand tracking. arXiv preprint arXiv:200610214. 2020. 26. Hong YNG, Ballekere AN, Fregly BJ, Roh J. Are muscle synergies useful for stroke rehabilitation? Current Opinion in Biomedical Engineering. 2021; 19: 100315. [ DOI:10.1016/j.cobme.2021.100315] 27. Hou Y, Xing S, Xu Q, editors. HALS-based algorithm for affine non-negative matrix factorization. 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC); 2014: IEEE. [ DOI:10.1109/SMC.2014.6974591] 28. Lugaresi C, Tang J, Nash H, McClanahan C, Uboweja E, Hays M, et al. Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:190608172. 2019. 29. d'Avella A, Saltiel P, Bizzi E. Combinations of muscle synergies in the construction of a natural motor behavior. Nature neuroscience. 2003; 6(3): 300-8. [ DOI:10.1038/nn1010] 30. Phan AH, Cichocki A. Extended HALS algorithm for nonnegative Tucker decomposition and its applications for multiway analysis and classification. Neurocomputing. 2011; 74(11): 1956-69. [ DOI:10.1016/j.neucom.2010.06.031] 31. Cichocki A, Phan AH, Caiafa C, editors. Flexible HALS algorithms for sparse non-negative matrix/tensor factorization. 2008 Ieee Workshop on Machine Learning for Signal Processing; 2008: IEEE. [ DOI:10.1109/MLSP.2008.4685458] 32. Diaconescu E. The use of NARX neural networks to predict chaotic time series. Wseas Transactions on computer research. 2008; 3(3): 182-91. 33. Dorffner G, editor Neural networks for time series processing. Neural network world; 1996: Citeseer. 34. Lin T, Horne BG, Tino P, Giles CL. Learning long-term dependencies in NARX recurrent neural networks. IEEE Transactions on Neural Networks. 1996; 7(6): 1329-38. [ DOI:10.1109/72.548162] 35. Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks. 1994; 5(2): 157-66. [ DOI:10.1109/72.279181] 36. Elman JL. Finding structure in time. Cognitive science. 1990; 14(2): 179-211. [ DOI:10.1207/s15516709cog1402_1] 37. Liu X, Lin Z, Feng Z. Short-term offshore wind speed forecast by seasonal ARIMA-A comparison against GRU and LSTM. Energy. 2021; 227: 120492. [ DOI:10.1016/j.energy.2021.120492] 38. Kubat M. Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7. The Knowledge Engineering Review. 1999; 13(4): 409-12. [ DOI:10.1017/S0269888998214044] 39. Menezes Jr JMP, Barreto GA. Long-term time series prediction with the NARX network: An empirical evaluation. Neurocomputing. 2008; 71(16-18): 3335-43. [ DOI:10.1016/j.neucom.2008.01.030] 40. Kubota S, Hara Y, Shimizu Y, Kadone H, Kubo T, Marushima A, et al. A newly developed upper limb single-joint HAL in a patient with elbow flexion reconstruction after traumatic brachial plexus injury: a case report. Interdisciplinary Neurosurgery. 2017; 10: 66-8. [ DOI:10.1016/j.inat.2017.07.005] 41. mazrooei e, azarnoosh m, ghoshuni m, khalilzadeh m. Comparison of the Function of the Elman Neural Network and the Deep Neural Network for the Diagnosis of Mild Alzheimer's Disease. The Neuroscience Journal of Shefaye Khatam. 2021; 10(1): 1-11. [ DOI:10.52547/shefa.10.1.1] 42. mazrooei rad e, pazhoumand h, salmani bajestani s. Separation of Healthy Individuals and Patients with Alzheimer's Disease Using the Effective Communication of Brain Signals. The Neuroscience Journal of Shefaye Khatam. 2022; 11(1): 1-12. [ DOI:10.52547/shefa.11.1.1] 43. Esmaili Jami A, Khalilzadeh MA, Ghoshuni M, Khalilzadeh MM. Estimation of Attention Indices in IVA Tests Using Optical Flow in ERP Brain Maps. The Neuroscience Journal of Shefaye Khatam. 2022; 11(1): 46-56. [ DOI:10.52547/shefa.11.1.46] 44. Majdi H, Azarnoosh M, Ghoshuni M, Sabzevari V. Using Visibility Graph to Analyze Brain Connectivity. The Neuroscience Journal of Shefaye Khatam. 2022; 10(2): 57-67. 45. oluki m, mahmoudi f, abdolmaleki a. Therapeutic Factors in Ischemic Stroke Control. The Neuroscience Journal of Shefaye Khatam. 2022; 10(4): 77-91. [ DOI:10.52547/shefa.10.4.77] 46. heyrani a, azizidarabkhani n. The Effects of Bilateral Motor Training on the Power of Grip in Affected Hand of Children with Spastic Hemiplegic Cerebral Palsy. The Neuroscience Journal of Shefaye Khatam. 2021; 9(3): 27-35. [ DOI:10.52547/shefa.9.3.27] |
