فیلتر نویز سیگنال های الکترومیوگرافی (EMG)

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• نوع فایل: Word (.docx)
• رشته: مهندسی پزشکی
• گرایش مرتبط: بیوالکتریک
• مقطع تحصیلی: کارشناسی ارشد
• عنوان: فیلتر نویز سیگنال‌های الکترومیوگرافی (EMG) – فصل دوم پایان‌نامه
• موضوع: بررسی مبانی نظری سیگنال‌های الکترومیوگرافی، منابع ایجاد نویز و آرتیفکت و روش‌های مختلف پردازش و فیلترکردن سیگنال EMG با هدف افزایش کیفیت و قابلیت تحلیل سیگنال.
• محتوای فصل: در فصل دوم، ابتدا مبانی فیزیولوژیکی تولید سیگنال الکترومیوگرافی و روش‌های ثبت سیگنال EMG بررسی می‌شود. سپس مشخصات زمانی و فرکانسی سیگنال، انواع نویز و آرتیفکت از جمله تداخل برق شهر، نویز ناشی از حرکت الکترود، نویز تجهیزات و سایر اختلالات موجود در فرایند ثبت معرفی می‌شوند. در ادامه، روش‌های متداول حذف نویز نظیر فیلترهای پایین‌گذر، بالاگذر، میان‌گذر و ناچ و همچنین روش‌های پیشرفته‌تر مانند فیلتر تطبیقی، تبدیل موجک و تکنیک‌های پردازش سیگنال بررسی و مقایسه می‌شوند. در پایان نیز معیارهای ارزیابی کیفیت سیگنال و عملکرد روش‌های نویززدایی تشریح می‌گردند.
• محورهای اصلی: سیگنال EMG، الکترومیوگرافی، پردازش سیگنال زیستی، حذف نویز، فیلتر دیجیتال، فیلتر ناچ، فیلتر تطبیقی، تبدیل موجک، آرتیفکت حرکتی، تداخل برق شهر، نسبت سیگنال به نویز
• حوزه تخصصی: مهندسی پزشکی، پردازش سیگنال‌های زیستی و بیوالکتریک
• جامعه مورد مطالعه: سیگنال‌های الکترومیوگرافی ثبت‌شده از عضلات افراد مورد مطالعه یا مجموعه‌داده‌های استاندارد EMG، متناسب با روش و داده‌های مورد استفاده در پژوهش
• فرمت: Word قابل ویرایش

• این مجموعه هیچ مسئولیتی در قبال متون نگارش شده ندارد.
• فایل صرفاً جهت کمک و تسهیل روند نگارش فصول می‌باشد و مسئولیت استفاده نهایی و صحت‌سنجی علمی بر عهده پژوهشگر است.

1. منابع و مراجع

   [1] P. Kaczmarek, T. Mánkowski, J. Tomczýnski, putEMG—a surface electromyography hand gesture recognition dataset, Sensors 19 (2019) 3548 2019, Vol. 19, Page 3548.

   [2] B. Chan, I. Saad, N. Bolong and K.E. Siew. A Review of Surface EMG in Clinical Rehabilitation Care Systems Design. In Proceedings of the 2021 IEEE 19th Student Conference on Research and Development (SCOReD), Kota Kinabalu, Malaysia, 23–25 November 2021; pp. 371–376.

   [3] D. Farina, D. Stegeman, R. Merletti, Biophysics of the Generation of EMG Signals, Surface Electromyography: Physiology, Engineering, and Applications, pp. 1-24, 2016.

   [4] I. Campanini, C. Disselhorst-Klug, W. Rymer and R. Merletti, Surface EMG in Clinical Assessment and Neurorehabilitation: Barriers Limiting Its Use, Front. Neurol. 11 (2020).

   [5] A. Asif, A. Waris, S. Gilani, M. Jamil, H. Ashraf, M. Shafique, I. Niazi, Performance evaluation of convolutional neural network for hand gesture recognition using EMG, Sensors 20 (6) (2020) 1642.

   [6] J. Maier, A. Naber, M. Ortiz-Catalan, Improved prosthetic control based on myoelectric pattern recognition via wavelet-based de-noising, in: IEEE Transactions on Neural Systems and Rehabilitation Engineering 26(2) (2018) 506- 514.

   [7] C. Zhang, T. Sun, Discussion of the influence of multiscale PCA denoising methods with three different features, Sensors 22 (4) (2022) 1604.

   [8] H. Ge, G. Chen, H. Yu, H. Chen, F. An, Theoretical analysis of empirical mode decomposition, Symmetry 10(11) (2018) 623.

   [9] K. Dragomiretskiy, D. Zosso, Variational mode decomposition, in: IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531-544, 2014.

   [10] Variational mode decomposition, Variational Mode Decomposition - an overview | ScienceDirect Topics. [Online].Available:www.sciencedirect.com/topics/ engineering/variational-mode-decomposition [Accessed: 23-Feb-2022].

   [11] G. Li, G. Tang, G. Luo, H. Wang, Underdetermined blind separation of bearing faults in hyperplane space with variational mode decomposition, Mechanical Systems and Signal Processing, vol. 120, pp. 83-97, 2019.

   [12] K. Hong, L. Wang, S. Xu, A variational mode decomposition approach for degradation assessment of power transformer windings, IEEE Trans. Instrum. Meas. 68 (4) (2019) 1221–1229.

   [13] Q. Wang, C. Yang, H. Wan, D. Deng, A. Nandi, Bearing fault diagnosis based on optimized variational mode decomposition and 1D convolutional neural networks, Meas. Sci. Technol. 32 (10) (2021), 104007.

   [14] S. Lahmiri, M. Boukadoum, Biomedical image denoising using variational mode decomposition. 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings, 2014.

   [15] S. Ma, B. Lv, C. Lin, X. Sheng and X. Zhu, “EMG Signal Filtering Based on Variational Mode Decomposition and Sub-Band Thresholding”, IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 1, pp. 47-58, 2021.

   [16] X. Xi, Y. Zhang, Y. Zhao, Q. She, Z. Luo, Denoising of surface electromyogram based on complementary ensemble empirical mode decomposition and improved interval thresholding“, Review of Scientific Instruments, vol. 90, no. 3, p. 035003, 2019.

   [17] E.N. Kamavuako, et al., On the usability of intramuscular EMG for prosthetic control: A Fitts’ law approach, J. Electromyography Kinesiol. 24 (Oct. 2014) 770–777.

   [18] optimized variational mode decomposition and 1D convolutional neural networks, Meas. Sci. Technol. 32 (10) (2021), 104007.

   [19] Z. Sun, X. Xi, C. Yuan, Y. Yang, X. Hua, Surface electromyography signal Denoising via EEMD and improved wavelet thresholds, Math. Biosci. Eng. 17 (6) (2020) 6945–6962.

   [20] X. Xi, Y. Zhang, Y. Zhao, Q. She, Z. Luo, Denoising of surface electromyogram based on complementary ensemble empirical mode decomposition and improved interval thresholding“, Review of Scientific Instruments, vol. 90, no. 3, p. 035003, 2019.

   [21] H. Ashraf, A. Waris, S.O. Gilani, M.U. Tariq, H. Alquhayz, Threshold Parameters Selection for Empirical Mode Decomposition-Based EMG Signal Denoising, Intelligent Automation Soft Computing 27 (3) (2021) 799–815.

   [22] A. Waris, I.K. Niazi, M. Jamil, K. Englehart, W. Jensen, et al., Multiday evaluation of techniques for EMG based classification of hand motions, IEEE J. Biomed. Health Inform. 23 (4) (2018) 1526–1534.

   [23] U. Raghavendra et al., “Automated screening of congestive heart failure using variational mode decomposition and texture features extracted from ultrasound images”, Neural Computing and Applications, vol. 28, no. 10, pp. 2869-2878, 2017.

   [24] M. Zhou et al., De-noising of photoacoustic sensing and imaging based on combined empirical mode decomposition and independent component analysis“, Journal of Biophotonics, vol. 12, no. 8, 2019.

   [25] A. Waris, M. Zia ur Rehman, I. Niazi, M. Jochumsen, K. Englehart, W. Jensen, H. Haavik and E. Kamavuako, 2020. A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN. Sensors, 20(12), p.3385.

   [26] Yu, Mohan. “Removal Methods of EMG Artifacts from EEG Signals.” Journal of Physics: Conference Series, vol. 1920,no. 1, 2021, p. 012076.

   [27] X. Xu, C. Xun, and Z. J. E. L. Yu, "Removal of Muscle Artifacts from Few-Channel EEG Recordings Based onMultivariate Empirical Mode Decomposition and Independent Vector Analysis," Electronics Letters, vol. 54, no. 14, 2018.

   [28] Chen et al., "The Use of Multivariate EMD and CCA for Denoising Muscle Artifacts From FewChannel EEG Recordings,"IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 2, pp. 359-370, 2018.

   [29] D. Esposito, J. Centracchio, P. Bifulco, and E. Andreozzi, “A smart approach to EMG envelope extraction and powerful denoising for human– Machine interfaces,” Sci. Rep., vol. 13, May 2023, Art. no. 7768.

   [30] V. Atanasoski et al., “A database of simultaneously recorded ECG signals with and without EMG noise,” IEEE Open J. Eng. Med. Biol., vol. 4, pp. 222–225, 2023.

   [31] Angeli, C. A., Boakye, M., Morton, R. A., Vogt, J., Benton, K., Chen, Y., et al. (2018). Recovery of over-ground walking after chronic motor complete spinal cord injury. New Engl. J. Med. 379, 1244–1250.

   [32] Grahn, P. J., Lavrov, I. A., Sayenko, D. G., Van Straaten, M. G., Gill, M. L., Strommen, J. A., et al. (2017). Enabling task-specific volitional motor functions via spinal cord neuromodulation in a human with paraplegia. Mayo Clin. Proc. 92, 544–554.

   [33] Grishin, A., Moshonkina, T., Solopova, I., Gorodnichev, R., and Gerasimenko, Y. (2017). A five-channel noninvasive electrical stimulator of the spinal cord for rehabilitation of patients with severe motor disorders. Biomed. Eng. 50, 300–304.

   [34] Kim, M., Chung, W. K., and Kim, K. (2020a). “Motion intensity extraction scheme for simultaneous recognition of wrist/hand motions,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), (Paris: IEEE), 10112–10117.

   [35] S. H. Yeon. Design of an advanced sEMG processor for wearable robotics applications. Master's thesis, Massachusetts Institute of Technology, 2019.

   [36] Seong Ho Yeon, Tony Shu, Hyungeun Song, Tsung-Han Hsieh, Junqing Qiao, Emily A Rogers, Samantha Gutierrez-Arango, Erica Israel, Lisa E Freed, and Hugh M Herr. Acquisition of surface emg using

   ---

   exible and low-prole electrodes for lower extremity neuroprosthetic control. IEEE Transactions on Medical Robotics and Bionics, 2021.

    

   [37] Kim, M., Chung, W. K., and Kim, K. (2020b). Subject-independent semg pattern recognition by using a muscle source activation model. IEEE Rob. Autom. Lett. 5, 5175–5180.

   [38] Britton JW, Frey LC, Hopp JL, Korb P, Koubeissi MZ, Lievens WE, et al. Electroencephalography (EEG): An Introductory Text and Atlas of Normal and Abnormal Findings in Adults, Children, and Infants. 1st ed. American Epilepsy Society; 2016.

   [39] Jirayucharoensak S, Israsena P, Pan-Ngum S, Hemrungrojn S, Maes M. A game-based neurofeedback training system to enhance cognitive performance in healthy elderly subjects and in patients with amnestic mild cognitive impairment. Clinical interventions in aging. 2019; 14:347–360.

   [40] Garcia-Casado J, Ye-Lin Y, Prats-Boluda G, Makeyev O. Evaluation of Bipolar, Tripolar, and Quadripolar Laplacian Estimates of Electrocardiogram via Concentric Ring Electrodes. Sensors. 2019; 19 (17):3780.

   [41] Aghaei-Lasboo A, Inoyama K, Fogarty AS, Kuo J, Meador KJ, Walter JJ, et al. Tripolar concentric EEG electrodes reduce noise. Clinical Neurophysiology. 2020; 131(1):193–198.

   [42] A. Zhou, S. R. Santacruz, B. C. Johnson, G. Alexandrov, A. Moin, F. L. Burghardt, J. M. Rabaey, J. M. Carmena, and R. Muller, ``A wireless and artefact-free 128-channel neuromodulation device for closed-loop stimulation and recording in non-human primates,'' Nature Biomed. Eng., vol. 3, no. 1, pp. 1526, 2019.

   [43] A. Zhou, B. C. Johnson, and R. Muller, ``Toward true closed-loop neuromodulation: Artifact-free recording during stimulation,'' Current Opinion Neurobiol., vol. 50, pp. 119127, Jun. 2018.

   [44] A. Shadmani, V. Viswam, Y. Chen, R. Bounik, J. Dragas, M. Radivojevic, S. Geissler, S. Sitnikov, J. Muller, and A. Hierlemann, ``Stimulation and artifact-suppression techniques for in vitro high-density microelectrode array systems,'' IEEE Trans. Biomed. Eng., vol. 66, no. 9, pp. 24812490, Sep. 2019.

   [45] Y. Li, J. Chen, and Y. Yang, ``A method for suppressing electrical stimulation artifacts from electromyography,'' Int. J. Neural Syst., vol. 29, no. 06, Aug. 2019, Art. no. 1850054.

   [46] Y. Zhou, Z. Bi, M. Ji, S. Chen, W. Wang, K. Wang, B. Hu, X. Lu, and Z. Wang, ``Adata-driven volitionalEMGextraction algorithm during functional electrical stimulation with time variant parameters,'' IEEE Trans. Neural Syst. Rehabil. Eng., vol. 28, no. 5, pp. 10691080, May 2020.

   [47] A Morphology-Preserving Algorithm for Denoising of EMG-Contaminated ECG Signals Vladimir Atanasoski , Jovana Petrovi´c , Member, IEEE, Lana Popovi´c Maneski , Marjan Mileti´c , Miloš Babi´c , Aleksandra Nikoli´c , Dorin Panescu, Fellow, IEEE, and Marija D. Ivanovi´c. IEEE Open J Eng Med Biol . 2024 Mar 25:5:296-305.

   [48] H. Ashraf, U. Shafiq, Q. Sajjad, A. Waris, O. Gilani, M. Variational mode decomposition for surface and intramuscular EMG signal denoising Boutaayamou a, O. Brüls a. Biomedical Signal Processing and Control 82 (2023) 104560.

   [49] C. Ouyang, L. Cai, B. Liu and T. Zhang. An improved wavelet threshold denoising approach for surface electromyography signal. EURASIP Journal on Advances in Signal Processing. (2023) 2023:108 https://doi.org/10.1186/s13634-023-01066-3.

   [50] D. Esposito, J. Centracchio, P. Bifulco and E. Andreozzi. A smart approach to EMG envelope extraction and powerful denoising for human–machine interfaces Electromyography (EMG) is widely used in human–machine interfaces (HMIs) to measure muscle. Scientific Reports. (2023), 13:7768. https://doi.org/10.1038/s41598-023-33319-4.

   [51] S. S. Motdhare, Dr. G. Mathur. An Experimental Analysis on EMG Artifact Removal Methods from EEG Signal Records. Mathematical Statistician and Engineering Applications. Page Number: 72 – 78, Issue: Vol 71 No. 1 (2022).

   [52] A. F. Hussein , W. R. Mohammed, M. M. Jaber and O. I. Khalaf. An Adaptive ECG Noise Removal Process Based on Empirical Mode Decomposition (EMD) Contrast Media & Molecular Imaging Volume 2022, Article ID 3346055, 9 pages.

   [53] Porr B, Daryanavard S, Bohollo LM, Cowan H, Dahiya R (2022) Real-time noise cancellation with deep learning. PLoS ONE 17(11): e0277974.

   [54] S. H. Yeon and H. M. Herr. Rejecting Impulse Artifacts from Surface EMG Signals using Real-time Cumulative Histogram Filtering 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Oct 31 - Nov 4, 2021. Virtual Conference.

   [55] I. Tamanna, Md. J. Nayeen Mahi, S. Ahmed, M. Kader, A. Haque, M. Biswas. Controlling Body Sources of Noise Generated by Niddle Electrogram Machines: A New EMG Idea for Skipping Traditional Approaches 2021 International Conference on Science & Contemporary Technologies (ICSCT). DOI: 10.1109/ICSCT53883.2021.9642546.

   [56] M. Kim, Y. Moon, J. Hunt, K.A. McKenzie, A. Horin, M. McGuire, K. Kim, L.J. Hargrove and A. Jayaraman. (2021). A Novel Technique to Reject Artifact Components for Surface EMG Signals Recorded During Walking With Transcutaneous Spinal Cord Stimulation: A Pilot Study. Front. Hum. Neurosci. 15:660583. doi: 10.3389/fnhum.2021.660583.

   [57] H.P. WANG, Z.Y. BI, W.J. FAN, Y.X. ZHOU, Y.X. ZHOU, F. LI and K. WANG. Digital Object Identifier 10.1109/ACCESS.2021.3077644 Real-Time Artifact Removal System for Surface EMG Processing During Ten-Fold Frequency Electrical Stimulation (Senior Member, IEEE). (Senior Member, IEEE) 1School of Electronic and Information Engineering, Sanjiang University, Nanjing 210012.

   [58] H. Ashraf, A. Waris, S. O. Gilani, M. U. Tariq and H. Alquhayz. Threshold Parameters Selection for Empirical Mode Decomposition-Based EMG Signal Denoising. Intelligent Automation & Soft Computing DOI:10.32604/iasc.2021.014765. IASC, 2021, vol.27, no.3.

   [59] De Luca, C.J.; Donald Gilmore, L.; Kuznetsov, M.; Roy, S.H. Filtering the surface EMG signal: Movement artifact and baseline noise contamination. J. Biomech. 2010, 43, 1573–1579.

   [60] Naik, G.R. Computational Intelligence in Electromyography Analysis: A Perspective on Current Applications and Future Challenges; InTech: London, UK, 2012.

   [61] Clancy, E.A.; Morin, E.L.; Merletti, R. Sampling, noise-reduction and amplitude estimation issues in surface electromyography. J. Electromyogr. Kinesiol. 2002, 12, 1–16.

   [62] Vijayvargiya, A.; Gupta, V.; Kumar, R.; Dey, N.; Tavares, J.M.R. A hybrid WD-EEMD sEMG feature extraction technique for lower limb activity recognition. IEEE Sens. J. 2021, 21, 20431–20439.

   [63] Merletti, R.; Cerone, G.L. Tutorial. Surface EMG detection, conditioning and pre-processing: Best practices. J. Electromyogr. Kinesiol. 2020, 54, 102440.

   [64] Stegeman, D.; Hermens, H. Standards for surface electromyography: The European project Surface EMG for non-invasive assessment of muscles (SENIAM). Enschede Roessingh Res. Dev. 2007, 10, 8–12.

   [65] Clancy, E.A.; Farry, K.A. Adaptive whitening of the electromyogram to improve amplitude estimation. IEEE. Trans. Biomed. Eng. 2000, 47, 709–719.

   [66] Li, C.; Li, G.; Jiang, G.; Chen, D.; Liu, H. Surface EMG data aggregation processing for intelligent prosthetic action recognition. Neural. Comput. Appl. 2020, 32, 16795–16806.

   [67] Schweitzer, T.; Fitzgerald, J.; Bowden, J.; Lynne-Davies, P. Spectral analysis of human inspiratory diaphragmatic electromyograms. J. Appl. Physiol. 1979, 46, 152–165.

   [68] Redfern, M.S.; Hughes, R.E.; Chaffin, D.B. High-pass filtering to remove electrocardiographic interference from torso EMG recordings. Clin. Biomech. 1993, 8, 44–48.

   [69] Reaz, M.B.I.; Hussain, M.S.; Mohd-Yasin, F. Techniques of EMG signal analysis: Detection, processing, classification and applications. Biol. Proced. Online 2006, 8, 11–35.

   [70] Bloch, R. Subtraction of electrocardiographic signal from respiratory electromyogram. J. Appl. Physiol. 1983, 55, 619–623.

   [71] Levine, S.; Gillen, J.; Weiser, P.; Gillen, M.; Kwatny, E. Description and validation of an ECG removal procedure for EMGdi power spectrum analysis. J. Appl. Physiol. 1986, 60, 1073–1081.

   [72] Bartolo, A.; Dzwonczyk, R.; Roberts, O.; Goldman, E. Description and validation of a technique for the removal of ECG contamination from diaphragmatic EMG signal. Med. Biol. Eng. Comput. 1996, 34, 76–81.

   [72] Junior, J.D.C.; de Seixas, J.M.; et al. A template subtraction method for reducing electrocardiographic artifacts in EMG signals of low intensity. Biomed. Signal Process. Control 2019, 47, 380–386.

   [73] Jonkman, A.H.; Juffermans, R.; Doorduin, J.; Heunks, L.M.; Harlaar, J. Estimated ECG Subtraction method for removing ECG artifacts in esophageal recordings of diaphragm EMG. Biomed. Signal Process. Control 2021, 69, 102861.

   [74] Conforto, S.; D’Alessio, T. Optimal rejection of artifacts in the processing of surface EMG signals for movement analysis. In Computer Methods in Biomechanics & Biomedical Engineering–2; CRC Press: Boca Raton, FL, USA, 2020; pp. 799–805.

   [75] Marker, R.J.; Maluf, K.S. Effects of electrocardiography contamination and comparison of ECG removal methods on upper trapezius electromyography recordings. J. Electromyogr. Kinesiol. 2014, 24, 902–909.

   [76] Yacoub, S.; Raoof, K. Noise removal from surface respiratory EMG signal. Int. J. Comput. Sci. Eng. 2008, 2, 226–233.

   [77] Widrow, B.; Glover, J.R.; McCool, J.M.; Kaunitz, J.;Williams, C.S.; Hearn, R.H.; Zeidler, J.R.; Dong, J.E.; Goodlin, R.C. Adaptive noise cancelling: Principles and applications. Proc. IEEE 1975, 63, 1692–1716.

   [78] Akkiraju, P.; Reddy, D. Adaptive cancellation technique in processing myoelectric activity of respiratory muscles. IEEE Trans. Biomed. Eng. 1992, 39, 652–655.

   [79] Lu, G.; Brittain, J.S.; Holland, P.; Yianni, J.; Green, A.L.; Stein, J.F.; Aziz, T.Z.;Wang, S. Removing ECG noise from surface EMG signals using adaptive filtering. Neurosci. Lett. 2009, 462, 14–19.

   [80] Marque, C.; Bisch, C.; Dantas, R.; Elayoubi, S.; Brosse, V.; Perot, C. Adaptive filtering for ECG rejection from surface EMG recordings. J. Electromyogr. Kinesiol. 2005, 15, 310–315.

   [81] Kim, M.; Moon, Y.; Hunt, J.; McKenzie, K.A.; Horin, A.; McGuire, M.; Kim, K.; Hargrove, L.J.; Jayaraman, A. A Novel Technique to Reject Artifact Components for Surface EMG Signals Recorded During Walking With Transcutaneous Spinal Cord Stimulation: A Pilot Study. Front. Hum. Neurosci. 2021, 15, 1–14.

   [82] Aschero, G.; Gizdulich, P. Denoising of surface EMG with a modifiedWiener filtering approach. J. Electromyogr. Kinesiol. 2010, 20, 366–373.

   [83] Djellatou, M.E.F.; Nougarou, F.; Massicotte, D. Enhanced FBLMS algorithm for ECG and noise removal from sEMG signals. In Proceedings of the 2013 18th International Conference on Digital Signal Processing (DSP), Corfu, Greece, 1–3 July 2013; IEEE: New York, NY, USA, 2013; pp. 1–6.

   [83] Wei, G.; Tian, F.; Tang, G.; Wang, C. A wavelet-based method to predict muscle forces from surface electromyography signals in weightlifting. J. Bionic Eng. 2012, 9, 48–58.

   [84] Mallat, S. A Wavelet Tour of Signal Processing; Elsevier: Amsterdam, The Netherlands, 1999.

   [85] Donoho, D.L.; Johnstone, J.M. Ideal spatial adaptation by wavelet shrinkage. Biometrika 1994, 81, 425–455.

   [86] Zhang, X.; Zhou, P. Filtering of surface EMG using ensemble empirical mode decomposition. Med. Eng. Phys. 2013, 35, 537–542.

   [87] Wu, Z.; Huang, N.E. Ensemble empirical mode decomposition: A noise-assisted data analysis method. Adv. Adapt Data Anal. 2009, 1, 1–41.

   [88] Sun, Z.; Xi, X.; Yuan, C.; Yang, Y.; Hua, X. Surface electromyography signal denoising via EEMD and improved wavelet thresholds. Math. Biosci. Eng. 2020, 17, 6945–6962.

   [89] Yeh, J.R.; Shieh, J.S.; Huang, N.E. Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method. Adv. Adapt Data Anal. 2010, 2, 135–156.

   [90] Xi, X.; Zhang, Y.; Zhao, Y.; She, Q.; Luo, Z. Denoising of surface electromyogram based on complementary ensemble empirical mode decomposition and improved interval thresholding. Rev. Sci. Instrum. 2019, 90, 035003.

   [91] Kopsinis, Y.; McLaughlin, S. Development of EMD-based denoising methods inspired by wavelet thresholding. IEEE Trans. Signal Process 2009, 57, 1351–1362.

   [92] Damasevicius, R.; Vasiljevas, M.; Martisius, I.; Jusas, V.; Birvinskas, D.;Wozniak, M. BoostEMD: An extension of EMD method and its application for denoising of EMG signals. Elektron. Ir. Elektrotechnika 2015, 21, 57–61.

   [93] Xiao, F.; Yang, D.; Guo, X.;Wang, Y. VMD-based denoising methods for surface electromyography signals. J. Neural Eng. 2019, 16, 056017.

   [94] Dragomiretskiy, K.; Zosso, D. Variational Mode Decomposition. IEEE Trans. Signal Process 2014, 62.

   [95] Ma, S.; Lv, B.; Lin, C.; Sheng, X.; Zhu, X. EMG signal filtering based on variational mode decomposition and sub-band thresholding. IEEE J. Biomed. Health Inform. 2020, 25, 47–58.

   [96] Polisiero, M. et al. Design and assessment of a low-cost, electromyographically controlled, prosthetic hand. Med. Devices (Auckl.) 6, 97–104.

   [97] MyoWare Muscle Sensor Kit—Learn.Sparkfun.Com. https:// learn. spark fun. com/ tutor ials/ myowa re- muscle- sensor- kit# myowa re- muscle- sensor (Accessed 27 January 2022).

   [98] D. L. Donoho and J. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika, vol. 81, no. 3, pp. 425–455, 1994.

   [99] Anas Abdulhameed Kzim, "Design and Implementation of a Robot Arm Driven by EMG Signal based on Microcontroller Unit", M.Sc. Thesis, University of Technology, October, 2014.

   [100] Çağdaş, Ö. Z. E. R., & ORMAN, Z. (2025). Enhancing EMG Signals for Amputee People with Deep Neural Network and Optimization Algorithms. MAS Journal of Applied Sciences, 10(1), 141-160.‏

   [101] Liu, Y. T., Wang, K. C., Liu, K. C., Peng, S. Y., & Tsao, Y. (2024, April). Sdemg: Score-based diffusion model for surface electromyographic signal denoising. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1736-1740). IEEE.

   ‏[102]  Oo, N., Aye, M. M., Oo, T., Win, L. L. Y., Tun, H., & Pradhan, D. (2024). Analysis of Electromyography (EMG) Signal Processing with Filtering Techniques. Journal of Novel Engineering Science and Technology, 3(02), 41-45.

   [103] Oo, Nandar & Myo Tun, Hla & Pradhan, Devasis & Aye, Mya & Oo, Thandar. (2024). Implementation of the Process for Contamination in Electromyography (EMG) Signal by Using Noise Removal Techniques. Journal of Novel Engineering Science and Technology. 3. 94-98. 10.56741/jnest.v3i03.627.

   [104] Ashraf, H., Shafiq, U., Sajjad, Q., Waris, A., Gilani, O., Boutaayamou, M., & Brüls, O. (2023). Variational mode decomposition for surface and intramuscular EMG signal denoising. Biomedical Signal Processing and Control, 82, 104560.

   ‏[105]  Elouaham, S., Nassiri, B., Dliou, A., Zougagh, H., El Kamoun, N., El Khadiri, K., & Said, S. (2023). Combination time-frequency and empirical wavelet transform methods for removal of composite noise in EMG signals. TELKOMNIKA (Telecommunication Computing Electronics and Control), 21(6), 1373-1381.‏

   [106] Nagasirisha, B., & Prasad, V. V. K. D. V. (2021). Emg signal denoising using adaptive filters through hybrid optimization algorithms. Biomedical Engineering: Applications, Basis and Communications, 33(02), 2150009.‏

   ‏[107]  Stachaczyk, M., Atashzar, S. F., & Farina, D. (2020). Adaptive spatial filtering of high-density EMG for reducing the influence of noise and artefacts in myoelectric control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(7), 1511-1517.

   ‏[108] Ladrova, M., Martinek, R., Nedoma, J., & Fajkus, M. (2019). Methods of power line interference elimination in EMG signal. Journal of Biomimetics, Biomaterials and Biomedical Engineering, 40, 64-70.

   ‏[109] Ghalyan, I. F., Abouelenin, Z. M., & Kapila, V. (2018, December). Gaussian filtering of EMG signals for improved hand gesture classification. In 2018 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) (pp. 1-6). IEEE.‏

   ]110[برفی، مهسا، کرمی، حمیدرضا، فراهی، الهام، فریدی، فاطمه، و حسینی پیلانگرگی، سیدمنوچهر. (1401). بهبود کنترل بازوی رباتیک به کمک کنترل کننده تطبیقی مدل مرجع با استفاده از طبقه بندی سیگنال های EMG. مهندسی برق و مهندسی کامپیوتر ایران - الف مهندسی برق، 20(3 )، 233-241. SID. https://sid.ir/paper/999226/fa

   ]111[عامری، علی. (1398). تشخیص حرکات مچ دست از روی سیگنال الکترومایوگرام با استفاده از شبکه عصبی کانولوشنال. مجله دانشکده پزشکی، 77(7 )، 434-439. SID. https://sid.ir/paper/365916/fa

   ]112[غلامی، عماد و غلامی، پریسا و طاهری، مهدی،1397،مقایسه روش ویولت و فیلتر ناچ در حذف نویز سیگنال EMG،دومین کنفرانس ملی پیشرفت های نوین در حوزه انرژی و صنایع نفت و گاز،ساوه،https://civilica.com/doc/786100

   ]113[انصاری، زهرا و کمالی، عباس،1394،شبیهسازی ساختار وینر ویولت جهت کاهش نویز الکترو میگو گرام - EMG (ازسیگنال الکتروکاردیوگرام)  ECG،دومین کنفرانس بین المللی پژوهش در مهندسی، علوم و تکنولوژی،https://civilica.com/doc/508296

   ‏[114] Buji, A. B., Mshelia, Y. P., Ibrahim, A. G., & Sarki, M. A. (2019). “Model Design, Simulation and Control of a Robotic Arm using PIC 16F877A Microcontroller”. Arid zone journal of engineering, technology and environment, 15(1), 67-76. (2019).

   ‏[115] Bai, Y., & Roth, Z.S.(2018). “Classical and  modern controls with microcontrollers: design, implementation and applications”. Springer. (2018).

   ‏[116] Li, G., Li, J., Ju, Z., Sun, Y., & Kong, J. (2019). “A novel feature extraction method for machine learning based on surface electromyography from healthy brain”. Neural Computing and Applications, 31(12), 9013-9022. (2019).

   [117] Mudiyanselage, S. W. H., & Lalitharatne, T. D. (2014, September). A study of controlling upper-limb exoskeletons using EMG and EEG signals. [Conference session].

   [118] Tan, L., & Jiang, J. (2018). Digital signal processing: Fundamentals and applications (3rd ed.). Academic Press.

   [119] Raurale, S. A., McAllister, J., & Del Rincon, J. M. (2020). Real-time embedded EMG signal analysis for wrist-hand pose identification. IEEE Transactions on Signal Processing, 68, 2713-2723. https://doi.org/10.1109/TSP.2020.2986993

   [120] Merletti, R., & Muceli, S. (2019). Tutorial: Surface EMG detection in space and time: Best practices. Journal of Electromyography and Kinesiology, 49, 102363. https://doi.org/10.1016/j.jelekin.2019.102363

   [121] Mortka, K., Wiertel-Krawczuk, A., & Lisinski, P. (2020). Muscle activity detectors—Surface electromyography in the evaluation of abductor hallucis muscle. Sensors, 20(8), 2162. https://doi.org/10.3390/s20082162

   [122] Ward, H. H. (2022). Programming Arduino projects with the PIC microcontroller. Apress.

   [123] Feldner, H. A., Howell, D., Kelly, V. E., McCoy, S. W., & Steele, K. M. (2019). "Look, your muscles are firing!": A qualitative study of clinician perspectives on the use of surface electromyography in neurorehabilitation. Archives of Physical Medicine and Rehabilitation, 100(4), 663-675. https://doi.org/10.1016/j.apmr.2018.11.011

   [124] Raez, M. B. I., Hussain, M. S., & Mohd-Yasin, F. (2006, March). Techniques of EMG signal analysis: Detection, processing, classification and applications. Biological Procedures Online, 8(1), 11-35. https://doi.org/10.1251/bpo114

   [125] Jamal, M. Z., Lee, D.-H., & Hyun, D. J. (2019, June). Real time adaptive filter based EMG signal processing and instrumentation scheme for robust signal acquisition using dry EMG electrodes. 2019 16th International Conference on Ubiquitous Robots (UR) (pp. 683-688). IEEE. https://doi.org/10.1109/URAI.2019.8768716

   [126] Wu, R., Zhang, H., Peng, T., Fu, L., & Zhao, J. (2019). Variable impedance interaction and demonstration interface design based on measurement of arm muscle co-activation for demonstration learning. Biomedical Signal Processing and Control, 51, 8-18. https://doi.org/10.1016/j.bspc.2019.02.003

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