Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).

College of Engineering The University of Texas in Arlington Arlington TX 76019 USA

Department of Cybernetics and Biomedical Engineering VSB Technical University Ostrava FEECS 708 00 Ostrava Czech Republic

Faculty of Electrical Engineering Opole University of Technology Automatic Control and Informatics 45 758 Opole Poland

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Martinek R., Ladrova M., Sidikova M., Jaros R., Behbehani K., Kahankova R., Kawala-Sterniuk A. Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part I: Cardiac Signals. Sensors. 2021;21:5186. doi: 10.3390/s21155186. PubMed DOI PMC

Rash G.S., Quesada P. Electromyography Fundamentals. Retrieved Febr. 2003;4

Manoni L., Turchetti C., Falaschetti L., Crippa P. A Comparative Study of Computational Methods for Compressed Sensing Reconstruction of Emg Signal. Sensors. 2019;19:3531. doi: 10.3390/s19163531. PubMed DOI PMC

Belyea A. Ph.D. Thesis. University of New Brunswick; Fredericton, NB, Canada: 2018. Evaluation of the Real-Time Usability of Force Myography as a Human-Computer Interface.

Kawala-Janik A., Podpora M., Konieczny M. Innovative Approach in Signal Processing of Electromyography Signals. J. Combat Sport. Martial Arts. 2014;5:101–112. doi: 10.5604/20815735.1141984. DOI

Rangayyan R.M. Biomedical Signal Analysis. 2nd ed. IEEE Press; Piscataway, NJ, USA: John Wiley & Sons, Inc.; Hoboken, NJ, USA: 2015. IEEE Press Series in Biomedical Engineering.

Penhaker M., Augustynek M. Zdravotnické Elektrické Přístroje 1. VSB—Technical University of Ostrava; Ostrava, Czechia: 2013.

Pasinetti S., Lancini M., Bodini I., Docchio F. A Novel Algorithm for EMG Signal Processing and Muscle Timing Measurement. IEEE Trans. Instrum. Meas. 2015;64:2995–3004. doi: 10.1109/TIM.2015.2434097. DOI

Shair E., Ahmad S., Abdullah A., Marhaban M., Tamrin S.M. Determining Best Window Size for an Improved Gabor Transform in EMG Signal Analysis. TELKOMNIKA Telecommun. Comput. Electron. Control. 2018;16:1650. doi: 10.12928/telkomnika.v16i4.9049. DOI

Reyes López D.A., Correa H.L., López M.A., Duarte Sánchez J.E. Expert Committee Classifier for Hand Motions Recognition from EMG Signals. INGENIARE Rev. Chil. De Ing. 2018;26:62–71. doi: 10.4067/S0718-33052018000100062. DOI

Dixon A.M., Allstot E.G., Gangopadhyay D., Allstot D.J. Compressed Sensing System Considerations for ECG and EMG Wireless Biosensors. IEEE Trans. Biomed. Circuits Syst. 2012;6:156–166. doi: 10.1109/TBCAS.2012.2193668. PubMed DOI

Salman A., Allstot E.G., Chen A.Y., Dixon A.M., Gangopadhyay D., Allstot D.J. Compressive Sampling of EMG Bio-Signals; Proceedings of the 2011 IEEE International Symposium of Circuits and Systems (ISCAS); Rio de Janeiro, Brazil. 15–18 May 2011; New York, NY, USA: IEEE; 2011. pp. 2095–2098.

Ravelomanantsoa A., Rouane A., Rabah H., Ferveur N., Collet L. Design and Implementation of a Compressed Sensing Encoder: Application to EMG and ECG Wireless Biosensors. Circuits Syst. Signal Process. 2017;36:2875–2892. doi: 10.1007/s00034-016-0444-y. DOI

Barkhaus P.E., Nandedkar S.D. Recording Characteristics of the Surface EMG Electrodes. Muscle Nerve. 1994;17:1317–1323. doi: 10.1002/mus.880171111. PubMed DOI

Celichowski J., Krutki P. Muscle and Exercise Physiology. Elsevier; Amsterdam, The Netherlands: 2019. Motor Units and Muscle Receptors; pp. 51–91. DOI

Merletti R., Parker P., editors. Electromyography: Physiology, Engineering, and Noninvasive Applications. IEEE Press; Piscataway, NJ, USA: John Wiley & Sons, Inc.; Hoboken, NJ, USA: 2004. IEEE Press Series in Biomedical Engineering.

Beverwyk A.J., Mancuso K., Prabhakar A., Lissauer J., Kaye A.D., Davis S.F. Electromyography (EMG) In: Davis S.F., Kaye A.D., editors. Principles of Neurophysiological Assessment, Mapping, and Monitoring. Springer International Publishing; Cham, Switzerland: 2020. pp. 135–145. DOI

Inbar G.F., Paiss O., Allin J., Kranz H. Monitoring Surface EMG Spectral Changes by the Zero Crossing Rate. Med. Biol. Eng. Comput. 1986;24:10–18. doi: 10.1007/BF02441600. PubMed DOI

Robertson D.G.E., editor. Research Methods in Biomechanics. Human Kinetics; Champaign, IL, USA: 2004.

Chamblin D. Foreword. Phys. Med. Rehabil. Clin. N. Am. 2015;26:xi–xiii. doi: 10.1016/j.pmr.2015.05.002. PubMed DOI

Pullman S.L., Goodin D.S., Marquinez A.I., Tabbal S., Rubin M. Clinical Utility of Surface EMG: Report of the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology. Neurology. 2000;55:171–177. doi: 10.1212/WNL.55.2.171. PubMed DOI

Tankisi H., Burke D., Cui L., de Carvalho M., Kuwabara S., Nandedkar S.D., Rutkove S., Stålberg E., van Putten M.J., Fuglsang-Frederiksen A. Standards of Instrumentation of EMG. Clin. Neurophysiol. 2020;131:243–258. doi: 10.1016/j.clinph.2019.07.025. PubMed DOI

Carvalho H., Verdonck M., Cools W., Geerts L., Forget P., Poelaert J. Forty Years of Neuromuscular Monitoring and Postoperative Residual Curarisation: A Meta-Analysis and Evaluation of Confidence in Network Meta-Analysis. Br. J. Anaesth. 2020;125:466–482. doi: 10.1016/j.bja.2020.05.063. PubMed DOI

Samuel O.W., Asogbon M.G., Geng Y., Al-Timemy A.H., Pirbhulal S., Ji N., Chen S., Fang P., Li G. Intelligent EMG Pattern Recognition Control Method for Upper-Limb Multifunctional Prostheses: Advances, Current Challenges, and Future Prospects. IEEE Access. 2019;7:10150–10165. doi: 10.1109/ACCESS.2019.2891350. DOI

Fang C., He B., Wang Y., Cao J., Gao S. EMG-Centered Multisensory Based Technologies for Pattern Recognition in Rehabilitation: State of the Art and Challenges. Biosensors. 2020;10:85. doi: 10.3390/bios10080085. PubMed DOI PMC

Kahl L., Hofmann U.G. Removal of ECG Artifacts from EMG Signals with Different Artifact Magnitudes by Template Subtraction. Curr. Dir. Biomed. Eng. 2019;5:357–359. doi: 10.1515/cdbme-2019-0090. DOI

Incorporated D. Signal Quality Monitor—EMGworks®. [(accessed on 15 July 2021)]. Available online: https://delsys.com/emgworks/signal-quality-monitor/

Abbaspour S., Fallah A. Removing ECG Artifact from the Surface EMG Signal Using Adaptive Subtraction Technique. J. Biomed. Phys. Eng. 2014;4:33. PubMed PMC

Chowdhury R., Reaz M., Ali M., Bakar A., Chellappan K., Chang T. Surface Electromyography Signal Processing and Classification Techniques. Sensors. 2013;13:12431–12466. doi: 10.3390/s130912431. PubMed DOI PMC

Kawala-Janik A. Ph.D. Thesis. University of Greenwich; London, UK: 2013. Efficiency Evaluation of External Environments Control Using Bio-Signals.

Kawala-Janik A., Pelc M., Podpora M. Method for EEG Signals Pattern Recognition in Embedded Systems. Elektron. Ir Elektrotech. 2015;21:3–9. doi: 10.5755/j01.eee.21.3.9918. DOI

Wu Q., Xi C., Ding L., Wei C., Ren H., Law R., Dong H., Li X.L. Classification of EMG Signals by BFA-Optimized GSVCM for Diagnosis of Fatigue Status. IEEE Trans. Autom. Sci. Eng. 2017;14:915–930. doi: 10.1109/TASE.2016.2564419. DOI

Lajante M.M.P., Droulers O., Amarantini D. How Reliable Are “State-of-the-Art” Facial EMG Processing Methods?: Guidelines for Improving the Assessment Of Emotional Valence in Advertising Research. J. Advert. Res. 2017;57:28–37. doi: 10.2501/JAR-2017-011. DOI

Soedirdjo S.D.H., Ullah K., Merletti R. Power Line Interference Attenuation in Multi-Channel sEMG Signals: Algorithms and Analysis; Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Milan, Italy. 25–29 August 2015; New York, NY, USA: IEEE; 2015. pp. 3823–3826. PubMed DOI

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. doi: 10.1016/j.neulet.2009.06.063. PubMed DOI

Marque C., Bisch C., Dantas R., Elayoubi S., Brosse V., Pérot C. Adaptive Filtering for ECG Rejection from Surface EMG Recordings. J. Electromyogr. Kinesiol. 2005;15:310–315. doi: 10.1016/j.jelekin.2004.10.001. PubMed DOI

Hussain M., Reaz M., Mohd-Yasin F., Ibrahimy M. Electromyography Signal Analysis Using Wavelet Transform and Higher Order Statistics to Determine Muscle Contraction. Expert Syst. 2009;26:35–48. doi: 10.1111/j.1468-0394.2008.00483.x. DOI

Jiang C.F., Kuo S.L. A Comparative Study of Wavelet Denoising of Surface Electromyographic Signals; Proceedings of the 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Lyon, France. 22–26 August 2007; New York, NY, USA: IEEE; 2007. pp. 1868–1871. PubMed DOI

Howard R.M., Conway R., Harrison A.J. An Exploration of Eliminating Cross-Talk in Surface Electromyography Using Independent Component Analysis; Proceedings of the 2015 26th Irish Signals and Systems Conference (ISSC); Carlow, Ireland. 24–25 June 2015; New York, NY, USA: IEEE; 2015. pp. 1–6. DOI

Naik G.R., Kumar D.K., Palaniswami M. Multi Run ICA and Surface EMG Based Signal Processing System for Recognising Hand Gestures; Proceedings of the 2008 8th IEEE International Conference on Computer and Information Technology; Sydney, Australia. 8–11 July 2008; New York, NY, USA: IEEE; 2008. pp. 700–705. DOI

Mengying X., Xiaoli Y., Chenli X., Bin Y. EMG Signal Processing and Application Based on Empirical Mode Decomposition. Signal. 2019;3:4. doi: 10.11648/j.mcs.20190406.11. DOI

Andrade A.O., Nasuto S., Kyberd P., Sweeney-Reed C.M., Van Kanijn F. EMG Signal Filtering Based on Empirical Mode Decomposition. Biomed. Signal Process. Control. 2006;1:44–55. doi: 10.1016/j.bspc.2006.03.003. DOI

Mishra V.K., Bajaj V., Kumar A., Sharma D., Singh G. An Efficient Method for Analysis of EMG Signals Using Improved Empirical Mode Decomposition. AEU Int. J. Electron. Commun. 2017;72:200–209. doi: 10.1016/j.aeue.2016.12.008. DOI

Zhang X., Zhou P. Filtering of Surface EMG Using Ensemble Empirical Mode Decomposition. Med. Eng. Phys. 2013;35:537–542. doi: 10.1016/j.medengphy.2012.10.009. PubMed DOI PMC

Abbaspour S., Lindén M., Gholamhosseini H. ECG Artifact Removal from Surface EMG Signal Using an Automated Method Based on Wavelet-ICA. PHealth. 2015;211:91–97. doi: 10.3233/978-1-61499-516-6-91. PubMed DOI

Ren X., Yan Z., Wang Z., Hu X. Noise Reduction Based on ICA Decomposition and Wavelet Transform for the Extraction of Motor Unit Action Potentials. J. Neurosci. Methods. 2006;158:313–322. doi: 10.1016/j.jneumeth.2006.06.005. PubMed DOI

Sinkjær T., Yoshida K., Jensen W., Schnabel V. Encyclopedia of Medical Devices and Instrumentation. John Wiley & Sons, Inc.; Hoboken, NJ, USA: 2006. Electroneurography.

Adrian E.D. The Basis of Sensation. W W Norton & Co.; New York, NY, USA: 1928. p. 122.

Dowben R.M., Rose J.E. A Metal-Filled Microelectrode. Science. 1953;118:22–24. doi: 10.1126/science.118.3053.22. PubMed DOI

Marg E., Adams J.E. Indwelling Multiple Micro-Electrodes in the Brain. Electroencephalogr. Clin. Neurophysiol. 1967;23:277–280. doi: 10.1016/0013-4694(67)90126-5. PubMed DOI

Langmeier J., Krejčířová D., Langmeier M. Vývojová Psychologie s Úvodem do Vývojové Neurofyziologie. H & H; Praha, Czech Republic: 1998.

Abdelghani M.N., Abbas J.J., Jung R. Peripheral Nerve Interface Applications, EMG/ENG. In: Jaeger D., Jung R., editors. Encyclopedia of Computational Neuroscience. Springer; New York, NY, USA: 2014. pp. 1–10. DOI

Hoffmann K.P., Krechel U. Devices and Methods in Clinical Neurophysiology. In: Kramme R., Hoffmann K.P., Pozos R.S., editors. Springer Handbook of Medical Technology. Springer; Berlin/Heidelberg, Germany: 2011. pp. 119–157. DOI

Lei G.S., Yang H.S., Miao J.T. The Clinical Significance of Neural Electrical Pathological Changes on Amyotrophic Lateral Sclerosis Patients. Mod. Rehabil. 2002

Nesrulaeva N., Gnezditskiy V., Demokidova O. P18. 11 Sensitivity and Specificity of Multimodal Evoked Potentials in the Diagnosis of Multiple Sclerosis and Optic Neuritis. Clin. Neurophysiol. 2011;122:140–141. doi: 10.1016/S1388-2457(11)60501-4. DOI

Dhillon G.S., Lawrence S.M., Hutchinson D.T., Horch K.W. Residual Function in Peripheral Nerve Stumps of Amputees: Implications for Neural Control of Artificial Limbs. J. Hand Surg. 2004;29:605–615. doi: 10.1016/j.jhsa.2004.02.006. PubMed DOI

Micera S., Carpaneto J., Raspopovic S. Control of Hand Prostheses Using Peripheral Information. IEEE Rev. Biomed. Eng. 2010;3:48–68. doi: 10.1109/RBME.2010.2085429. PubMed DOI

Navarro X., Krueger T.B., Lago N., Micera S., Stieglitz T., Dario P. A Critical Review of Interfaces with the Peripheral Nervous System for the Control of Neuroprostheses and Hybrid Bionic Systems. J. Peripher. Nerv. Syst. 2005;10:229–258. doi: 10.1111/j.1085-9489.2005.10303.x. PubMed DOI

Szczęsna A., Błaszczyszyn M., Kawala-Sterniuk A. Convolutional neural network in upper limb functional motion analysis after stroke. PeerJ. 2020;8:e10124. doi: 10.7717/peerj.10124. PubMed DOI PMC

Schaumberg J., Schwandt D. Movement Disorders of the Upper Extremities in Children. Springer; Berlin/Heidelberg, Germany: 2021. “Invasive” Diagnostic Procedures: Electromyography, Neurography and Evoked Potentials; pp. 85–91.

Ahmadizadeh C., Khoshnam M., Menon C. Human Machine Interfaces in Upper-Limb Prosthesis Control: A Survey of Techniques for Preprocessing and Processing of Biosignals. IEEE Signal Process. Mag. 2021;38:12–22. doi: 10.1109/MSP.2021.3057042. DOI

Bosl W. Neurotechnology and Psychiatric Biomarkers. In: Ghista D.N., editor. Biomedical Science, Engineering and Technology. InTechOpen; London, UK: 2012. DOI

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. doi: 10.1251/bpo115. PubMed DOI PMC

Hinton G.E. Machine Learning for Neuroscience. Neural Syst. Circuits. 2011;1:12. doi: 10.1186/2042-1001-1-12. PubMed DOI PMC

Lewicki M.S. A Review of Methods for Spike Sorting: The Detection and Classification of Neural Action Potentials. Network Comput. Neural Syst. 1998;9:R53–R78. doi: 10.1088/0954-898X_9_4_001. PubMed DOI

Robert P.Y., Sawan M. An Independent-Component-Analysis-Based Time-Space Processor for the Identification of Neural Stimulation Sources; Proceedings of the 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Lyon, France. 22–26 August 2007; New York, NY, USA: IEEE; 2007. pp. 3876–3879. PubMed DOI

Moshou D., Hostens I., Papaioannou G., Ramon H. Scattering Theory and Biomedical Engineering Modelling and Applications. WORLD SCIENTIFIC; Perdika, Greece: 2000. Wavelet Based Electromyogram (EMG) Analysis; pp. 265–272. DOI

Aschero G., Gizdulich P. Denoising of Surface EMG with a Modified Wiener Filtering Approach. J. Electromyogr. Kinesiol. 2010;20:366–373. doi: 10.1016/j.jelekin.2009.02.003. PubMed DOI

Lehky S.R. Decoding Poisson Spike Trains by Gaussian Filtering. Neural Comput. 2010;22:1245–1271. doi: 10.1162/neco.2009.07-08-823. PubMed DOI

Massot C., Schneider A.D., Chacron M.J., Cullen K.E. The Vestibular System Implements a Linear–Nonlinear Transformation in Order to Encode Self-Motion. PLoS Biol. 2012;10:e1001365. doi: 10.1371/journal.pbio.1001365. PubMed DOI PMC

Komorowski D., Pietraszek S., Tkacz E., Provaznik I. The Extraction of the New Components from Electrogastrogram (EGG), Using Both Adaptive Filtering and Electrocardiographic (ECG) Derived Respiration Signal. BioMedical Eng. Online. 2015;14:60. doi: 10.1186/s12938-015-0054-0. PubMed DOI PMC

Wang B., Wen T., Hou J., Lin L., Fang Y., Du Y., Pan T. The High-Dimensional Signal Classification of Electrogastrogram for Detection of Gastric Motility Disorders. J. Med. Imaging Health Inform. 2020;10:1281–1287. doi: 10.1166/jmihi.2020.3031. DOI

Riezzo G., Russo F., Indrio F. Electrogastrography in Adults and Children: The Strength, Pitfalls, and Clinical Significance of the Cutaneous Recording of the Gastric Electrical Activity. BioMed Res. Int. 2013;2013:282757. doi: 10.1155/2013/282757. PubMed DOI PMC

Komorowski D., Mika B. A New Approach for Denoising Multichannel Electrogastrographic Signals. Biomed. Signal Process. Control. 2018;45:213–224. doi: 10.1016/j.bspc.2018.05.041. DOI

Verhagen M.A.M.T. Electrogastrography. Clin. Auton. Res. 2005;15:364–367. doi: 10.1007/s10286-005-0313-4. PubMed DOI

Yin J., Chen J.D.Z. Electrogastrography: Methodology, Validation and Applications. J. Neurogastroenterol. Motil. 2013;19:5–17. doi: 10.5056/jnm.2013.19.1.5. PubMed DOI PMC

Alagumariappan P., Krishnamurthy K., Kandiah S., Cyril E., Venkatesan R. Feature Extraction and Genetic Algorithm Based Feature Selection for Diagnosis of Type-2 Diabetes Using Electrogastrograms. JMIR Biomed. Eng. 2020 doi: 10.2196/20932. DOI

Hu Y., Zhang B., Shi X., Ning B., Shi J., Zeng X., Liu F., Chen J.D., Xie W.F. Ameliorating Effects and Autonomic Mechanisms of Transcutaneous Electrical Acustimulation in Patients With Gastroesophageal Reflux Disease. NeuroModul. Technol. Neural Interface. 2019:1207–1214. doi: 10.1111/ner.13082. PubMed DOI

Levanon D., Chen J.Z. Electrogastrography: Its Role in Managing Gastric Disorders. J. Pediatr. Gastroenterol. Nutr. 1998;27:431–443. doi: 10.1097/00005176-199810000-00014. PubMed DOI

Komorowski D., Tkacz E. A New Method for Attenuation of Respiration Artifacts in Electrogastrographic (EGG) Signals; Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Milan, Italy. 25–29 August 2019; New York, NY, USA: IEEE; 2015. pp. 6006–6009. PubMed DOI

Chen J., Vandewalle J., Sansen W., Vantrappen G., Janssens J. Multichannel Adaptive Enhancement of the Electrogastrogram. IEEE Trans. Biomed. Eng. 1990;37:285–294. doi: 10.1109/10.52329. PubMed DOI

Qiao W., Sun H.H., Chey W.Y., Lee K.Y. Continuous Wavelet Analysis as an Aid in the Representation and Interpretation of Electrogastrographic Signals. Ann. Biomed. Eng. 1998;26:1072–1081. doi: 10.1114/1.27. PubMed DOI

Ryu C., Nam K., Kim S., Kim D. Comparison of Digital Filters with Wavelet Multiresolution Filter for Electrogastrogram; Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society Engineering in Medicine and Biology; Houston, TX, USA. 23–26 October 2002; New York, NY, USA: IEEE; 2002. pp. 137–138. DOI

De Sobral Cintra R., Tchervensky I., Dimitrov V., Mintchev M. Optimal Wavelets for Electrogastrography; Proceedings of the The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; San Francisco, CA, USA. 1–5 September 2004; New York, NY, USA: IEEE; 2004. pp. 329–332. PubMed DOI

Zou L., Zhang Y., Yang L.T., Zhou R. Single-Trial Evoked Potentials Study by Combining Wavelet Denoising and Principal Component Analysis Methods. J. Clin. Neurophysiol. 2010;27:17–24. doi: 10.1097/WNP.0b013e3181c9b29a. PubMed DOI

Hubka P., Rosik V., Zdinak J., Tysler M., Hulín I. Independent Component Analysis of Electrogastrographic Signals. Meas. Sci. Rev. 2005;5:21–24.

Liang H., Lin Z., McCallum R.W. Artifact Reduction in Electrogastrogram Based on Empirical Mode Decomposition Method. Med. Biol. Eng. Comput. 2000;38:35–41. doi: 10.1007/BF02344686. PubMed DOI

Kawala-Sterniuk A., Podpora M., Pelc M., Blaszczyszyn M., Gorzelanczyk E.J., Martinek R., Ozana S. Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes. Sensors. 2020;20:807. doi: 10.3390/s20030807. PubMed DOI PMC

Popovic N.B., Miljkovic N., Sekara T.B. Electrogastrogram and Electrocardiogram Interference: Application of Fractional Order Calculus and Savitzky-Golay Filter for Biosignals Segregation; Proceedings of the 2020 19th International Symposium INFOTEH-JAHORINA (INFOTEH); East Sarajevo, Bosnia and Herzegovina. 18–20 March 2020; New York, NY, USA: IEEE; 2020. pp. 1–5. DOI

Sengottuvel S., Khan P.F., Mariyappa N., Patel R., Saipriya S., Gireesan K. A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition. SLAS TECHNOLOGY: Transl. Life Sci. Innov. 2018;23:269–280. doi: 10.1177/2472630318756903. PubMed DOI

Heide W., Koenig E., Trillenberg P., K’ompf D., Zee D.S. Electrooculography: Technical Standards and Applications. Electroencephalogr. Clin. Neurophysiol. Suppl. 1999;52:223–240. PubMed

Agarwal S., Singh V., Rani A., Mittal A. Hardware Efficient Denoising System for Real EOG Signal Processing. J. Intell. Fuzzy Syst. 2017;32:2857–2862. doi: 10.3233/JIFS-169228. DOI

López A., Ferrero F., Villar J.R., Postolache O. High-Performance Analog Front-End (AFE) for EOG Systems. Electronicsweek. 2020;9:970. doi: 10.3390/electronics9060970. DOI

Creel D.J. Handbook of Clinical Neurology. Volume 160. Elsevier; Amsterdam, The Netherlands: 2019. The Electrooculogram; pp. 495–499. PubMed DOI

Reddy M.S., Sammaiah A., Narsimha B., Rao K.S. Analysis of EOG Signals Using Empirical Mode Decomposition for Eye Blink Detection; Proceedings of the 2011 International Conference on Multimedia and Signal Processing; Guilin, China. 14–15 May 2011; New York, NY, USA: IEEE; 2011. pp. 293–297. DOI

Sharma K., Jain N., Pal P.K. Detection of Eye Closing/Opening from EOG and Its Application in Robotic Arm Control. Biocybern. Biomed. Eng. 2020;40:173–186. doi: 10.1016/j.bbe.2019.10.004. DOI

Chang W.D., Cha H.S., Kim D.Y., Kim S.H., Im C.H. Development of an Electrooculogram-Based Eye-Computer Interface for Communication of Individuals with Amyotrophic Lateral Sclerosis. J. NeuroEng. Rehabil. 2017;14:89. doi: 10.1186/s12984-017-0303-5. PubMed DOI PMC

Tsai J., Lee C., Wu C., Wu J., Kao K. A Feasibility Study of an Eye-Writing System Based on Electro-Oculography. J. Med. Biol. Eng. 2008;28:39.

Fang F., Shinozaki T. Electrooculography-Based Continuous Eye-Writing Recognition System for Efficient Assistive Communication Systems. PLoS ONE. 2018;13:e0192684. doi: 10.1371/journal.pone.0192684. PubMed DOI PMC

Maddirala A., Shaik R.A. Removal of EOG Artifacts from Single Channel EEG Signals Using Combined Singular Spectrum Analysis and Adaptive Noise Canceler. IEEE Sen. J. 2016;16:8279–8287. doi: 10.1109/JSEN.2016.2560219. DOI

Najarian K. Biomedical Signal and Image Processing. CRC Press—Taylor & Francis Group; Boca Raton, FL, USA: 2016.

Deng L.Y., Hsu C.L., Lin T.C., Tuan J.S., Chang S.M. EOG-based Human–Computer Interface system development. Expert Syst. Appl. 2010;37:3337–3343. doi: 10.1016/j.eswa.2009.10.017. DOI

Keskinoğlu C., AYDIN A. EOG–Based Computer Control System for People with Mobility Limitations. Avrupa Bilim Teknol. Derg. 2021;26:256–261.

Milanizadeh S., Safaie J. EOG-based HCI system for quadcopter navigation. IEEE Trans. Instrum. Meas. 2020;69:8992–8999. doi: 10.1109/TIM.2020.3001411. DOI

He S., Li Y. A Single-Channel EOG-Based Speller. IEEE Trans. Neural Syst. Rehabil. Eng. 2017;25:1978–1987. doi: 10.1109/TNSRE.2017.2716109. PubMed DOI

Reddy M.S., Narasimha B., Suresh E., Rao K.S. Analysis of EOG Signals Using Wavelet Transform for Detecting Eye Blinks; Proceedings of the 2010 International Conference on Wireless Communications & Signal Processing (WCSP); Suzhou, China. 21–23 October 2010; New York, NY, USA: IEEE; 2010. pp. 1–4. DOI

Abbas S.N., Abo-Zahhad M. Eye Blinking EOG Signals as Biometrics. In: Jiang R., Al-maadeed S., Bouridane A., Crookes P.D., Beghdadi A., editors. Biometric Security and Privacy. Springer International Publishing; Cham, Switzerland: 2017. pp. 121–140. DOI

Divjak M., Bischof H. Eye Blink Based Fatigue Detection for Prevention of Computer Vision Syndrome; Proceedings of the 2009 IAPR Conference on Machine Vision Applications (MVA2009); Yokohama, Japan. 20–22 May 2009; pp. 350–353.

Grauman K., Betke M., Gips J., Bradski G. Communication via Eye Blinks—Detection and Duration Analysis in Real Time; Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001; Kauai, HI, USA. 8–14 December 2001; Washington, DC, USA: IEEE Computer Society; 2001. pp. I-1010–I-1017. DOI

Ebrahim P. Ph.D. Thesis. University of Stuttgart; Stuttgart, Germany: 2016. Driver Drowsiness Monitoring Using Eye Movement Features Derived from Electrooculography. DOI

Torricelli D., Goffredo M., Conforto S., Schmid M. An Adaptive Blink Detector to Initialize and Update a View-Basedremote Eye Gaze Tracking System in a Natural Scenario. Pattern Recognit. Lett. 2009;30:1144–1150. doi: 10.1016/j.patrec.2009.05.014. DOI

Jammes B., Sharabty H., Esteve D. Automatic EOG Analysis: A First Step toward Automatic Drowsiness Scoring during Wake-Sleep Transitions. Somnologie Schlafforschung Schlafmed. 2008;12:227–232. doi: 10.1007/s11818-008-0351-y. DOI

Picot A., Caplier A., Charbonnier S. Comparison between EOG and High Frame Rate Camera for Drowsiness Detection; Proceedings of the 2009 Workshop on Applications of Computer Vision (WACV); Snowbird, UT, USA. 7–8 December 2009; New York, NY, USA: IEEE; 2009. pp. 1–6. DOI

Picot A., Charbonnier S., Caplier A. On-Line Detection of Drowsiness Using Brain and Visual Information. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 2012;42:764–775. doi: 10.1109/TSMCA.2011.2164242. DOI

Schmidt J., Laarousi R., Stolzmann W., Karrer-Gauß K. Eye Blink Detection for Different Driver States in Conditionally Automated Driving and Manual Driving Using EOG and a Driver Camera. Behav. Res. Methods. 2018;50:1088–1101. doi: 10.3758/s13428-017-0928-0. PubMed DOI

Issa M.F., Juhasz Z. Improved EOG Artifact Removal Using Wavelet Enhanced Independent Component Analysis. Brain Sci. 2019;9:355. doi: 10.3390/brainsci9120355. PubMed DOI PMC

Maddirala A.K., Veluvolu K.C. Eye-Blink Artifact Removal from Single Channel EEG with k-Means and SSA. Sci. Rep. 2021;11:11043. doi: 10.1038/s41598-021-90437-7. PubMed DOI PMC

Joyce C.A., Gorodnitsky I.F., Kutas M. Automatic Removal of Eye Movement and Blink Artifacts from EEG Data Using Blind Component Separation. Psychophysiology. 2004;41:313–325. doi: 10.1111/j.1469-8986.2003.00141.x. PubMed DOI

Gao J.F., Yang Y., Lin P., Wang P., Zheng C.X. Automatic Removal of Eye-Movement and Blink Artifacts from EEG Signals. Brain Topogr. 2010;23:105–114. doi: 10.1007/s10548-009-0131-4. PubMed DOI

Shahabi H., Moghimi S., Zamiri-Jafarian H. EEG Eye Blink Artifact Removal by EOG Modeling and Kalman Filter; Proceedings of the 2012 5th International Conference on BioMedical Engineering and Informatics; Chongqing, China. 16–18 October 2012; New York, NY, USA: IEEE; 2012. pp. 496–500. DOI

Gotch F. The Time Relations of the Photo-Electric Changes in the Eyeball of the Frog. J. Physiol. 1903;29:388–410. doi: 10.1113/jphysiol.1903.sp000965. PubMed DOI PMC

Einthoven W., Jolly W.A. The Form and Magnitude of the Electrical Response of the Eye to Stimulation by Light at Various Intensities. Q. J. Exp. Physiol. 1908;1:373–416. doi: 10.1113/expphysiol.1908.sp000026. DOI

Heckenlively J.R., Arden G.B., editors. Principles and Practice of Clinical Electrophysiology of Vision. 2nd ed. MIT Press; Cambridge, MA, USA: 2006.

Brigell M., Bach M., Barber C., Kawasaki K., Kooijman A. Guidelines for Calibration of Stimulus and Recording Parameters Used in Clinical Electrophysiology of Vision. Doc. Ophthalmol. 1998;95:1–14. doi: 10.1023/A:1001724411607. PubMed DOI

Johnson M.A., Massof R.W. The Photomyoclonic Reflex: An Artefact in the Clinical Electroretinogram. Br. J. Ophthalmol. 1982;66:368–378. doi: 10.1136/bjo.66.6.368. PubMed DOI PMC

Yip Y.W.Y., Man T.C., Pang C.P., Brelén M.E. Improving the Quality of Electroretinogram Recordings Using Active Electrodes. Exp. Eye Res. 2018;176:46–52. doi: 10.1016/j.exer.2018.06.007. PubMed DOI

Latifoglu F., Guven A., Durmus U., Oner A. Denoising of Electroretinogram Signals Using Empirical Mode Decomposition; Proceedings of the 2010 15th National Biomedical Engineering Meeting; Antalya, Turkey. 21–24 April 2010; New York, NY, USA: IEEE; 2010. pp. 1–4. DOI

Barraco R., Adorno D.P., Brai M., Tranchina L. A Comparison among Different Techniques for Human ERG Signals Processing and Classification. Phys. Medica. 2014;30:86–95. doi: 10.1016/j.ejmp.2013.03.006. PubMed DOI

De Santiago L., Ortiz del Castillo M., Garcia-Martin E., Rodrigo M.J., Sanchez Morla E.M., Cavaliere C., Cordón B., Miguel J.M., López A., Boquete L. Empirical Mode Decomposition-Based Filter Applied to Multifocal Electroretinograms in Multiple Sclerosis Diagnosis. Sensors. 2019;20:7. doi: 10.3390/s20010007. PubMed DOI PMC

Sarossy M., Aliahmad B., Kumar D.K. Discrete Wavelet Transform of the Electroretinogram for Glaucoma Classification; Choice of Mother Wavelet by Variable Ranking |IOVS| ARVO Journals. Investig. Ophthalmol. Vis. Sci. 2018;59:5098. PubMed

Adithya P.C., Alaql A., Tzekov R., Sankar R., Moreno W.A. Model Based Photopic Electroretinogram Source Separation: A Multiresolution Analysis Approach; Proceedings of the 2017 International Caribbean Conference on Devices, Circuits and Systems (ICCDCS); Cozumel, Mexico. 5–7 June 2017; New York, NY, USA: IEEE; 2017. pp. 21–24. DOI

Ebdali S., Hashemi B., Jafarzadehpour E. Comparing the Variation of Time and Frequency Components of Electroretinogram in Patients with Retinitis Pigmentosa and Healthy Individuals. J. Mazandaran Univ. Med. Sci. 2017;26:110–121.

Vinken M.P.G.C., Rabotti C., Mischi M., Oei S.G. Accuracy of Frequency-Related Parameters of the Electrohysterogram for Predicting Preterm Delivery: A Review of the Literature. Obstet. Gynecol. Surv. 2009;64:529–541. doi: 10.1097/OGX.0b013e3181a8c6b1. PubMed DOI

Miles A.M., Monga M., Richeson K.S. Correlation of External and Internal Monitoring of Uterine Activity in a Cohort of Term Patients. Am. J. Perinatol. 2001;18:137–140. doi: 10.1055/s-2001-14522. PubMed DOI

Lucovnik M., Maner W.L., Chambliss L.R., Blumrick R., Balducci J., Novak-Antolic Z., Garfield R.E. Noninvasive Uterine Electromyography for Prediction of Preterm Delivery. Am. J. Obstet. Gynecol. 2011;204:228.e1–228.e10. doi: 10.1016/j.ajog.2010.09.024. PubMed DOI PMC

Euliano T.Y., Marossero D., Nguyen M.T., Euliano N.R., Principe J., Edwards R.K. Spatiotemporal Electrohysterography Patterns in Normal and Arrested Labor. Am. J. Obstet. Gynecol. 2009;200:54.e1–54.e7. doi: 10.1016/j.ajog.2008.09.008. PubMed DOI

Rabotti C., Mischi M., van Laar J.O.E.H., Oei G.S., Bergmans J.W.M. Inter-Electrode Delay Estimators for Electrohysterographic Propagation Analysis. Physiol. Meas. 2009;30:745–761. doi: 10.1088/0967-3334/30/8/002. PubMed DOI

Rabotti C., Mischi M. Two-Dimensional Estimation of the Electrohysterographic Conduction Velocity; Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology; Buenos Aires, Argentina. 31 August–4 September 2010; New York, NY, USA: IEEE; 2010. pp. 4262–4265. PubMed DOI

Ye-Lin Y., Garcia-Casado J., Prats-Boluda G., Alberola-Rubio J., Perales A. Automatic Identification of Motion Artifacts in EHG Recording for Robust Analysis of Uterine Contractions. Comput. Math. Methods Med. 2014;2014:470786. doi: 10.1155/2014/470786. PubMed DOI PMC

Marque C., Duchene J.M.G., Leclercq S., Panczer G.S., Chaumont J. Uterine EHG Processing for Obstetrical Monitorng. IEEE Trans. Biomed. Eng. 1986;BME-33:1182–1187. doi: 10.1109/TBME.1986.325698. PubMed DOI

Gondry J., Marque C., Duchene J., Cabrol D. Electrohysterography during Pregnancy: Preliminary Report. Biomed. Instrum. Technol. 1993;27:318–324. PubMed

Alberola-Rubio J., Garcia-Casado J., Ye-Lin Y., Prats-Boluda G., Perales A. Recording of Electrohysterogram Laplacian Potential; Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Boston, MA, USA. 30 August–3 September 2011; New York, NY, USA: IEEE; 2011. pp. 2510–2513. PubMed DOI

Rabotti C., Mischi M., van Laar J.O.E.H., Oei G.S., Bergmans J.W.M. Estimation of Internal Uterine Pressure by Joint Amplitude and Frequency Analysis of Electrohysterographic Signals. Physiol. Meas. 2008;29:829–841. doi: 10.1088/0967-3334/29/7/011. PubMed DOI

Hassan M.M., Terrien J., Muszynski C., Alexandersson A., Marque C., Karlsson B. Better Pregnancy Monitoring Using Nonlinear Correlation Analysis of External Uterine Electromyography. IEEE Trans. Biomed. Eng. 2013;60:1160–1166. doi: 10.1109/TBME.2012.2229279. PubMed DOI

Marque C.K., Terrien J., Rihana S., Germain G. Preterm Labour Detection by Use of a Biophysical Marker: The Uterine Electrical Activity. BMC Pregnancy Childbirth. 2007;7:S5. doi: 10.1186/1471-2393-7-S1-S5. PubMed DOI PMC

Euliano T.Y., Nguyen M.T., Darmanjian S., McGorray S.P., Euliano N., Onkala A., Gregg A.R. Monitoring Uterine Activity during Labor: A Comparison of 3 Methods. Am. J. Obstet. Gynecol. 2013;208:66.e1–66.e6. doi: 10.1016/j.ajog.2012.10.873. PubMed DOI PMC

Euliano T.Y., Nguyen M.T., Marossero D., Edwards R.K. Monitoring Contractions in Obese Parturients: Electrohysterography Compared With Traditional Monitoring. Obstet. Gynecol. 2007;109:1136–1140. doi: 10.1097/01.AOG.0000258799.24496.93. PubMed DOI

Jezewski J., Horoba K., Matonia A., Wrobel J. Quantitative Analysis of Contraction Patterns in Electrical Activity Signal of Pregnant Uterus as an Alternative to Mechanical Approach. Physiol. Meas. 2005;26:753–767. doi: 10.1088/0967-3334/26/5/014. PubMed DOI

Jacod B.C., Graatsma E.M., Van Hagen E., Visser G.H.A. A Validation of Electrohysterography for Uterine Activity Monitoring during Labour. J. Matern. Fetal Neonatal Med. 2010;23:17–22. doi: 10.3109/14767050903156668. PubMed DOI

Schlembach D., Maner W.L., Garfield R.E., Maul H. Monitoring the Progress of Pregnancy and Labor Using Electromyography. Eur. J. Obstet. Gynecol. Reprod. Biol. 2009;144:S33–S39. doi: 10.1016/j.ejogrb.2009.02.016. PubMed DOI

Garcia-Gonzalez M.T., Charleston-Villalobos S., Vargas-Garcia C., Gonzalez-Camarena R., Aljama-Corrales T. Characterization of EHG Contractions at Term Labor by Nonlinear Analysis; Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Osaka, Japan. 3–7 July 2013; New York, NY, USA: IEEE; 2013. pp. 7432–7435. PubMed DOI

Acharya U.R., Sudarshan V.K., Rong S.Q., Tan Z., Lim C.M., Koh J.E., Nayak S., Bhandary S.V. Automated Detection of Premature Delivery Using Empirical Mode and Wavelet Packet Decomposition Techniques with Uterine Electromyogram Signals. Comput. Biol. Med. 2017;85:33–42. doi: 10.1016/j.compbiomed.2017.04.013. PubMed DOI

Leman H., Marque C. Rejection of the Maternal Electrocardiogram in the Electrohysterogram Signal. IEEE Trans. Biomed. Eng. 2000;47:1010–1017. doi: 10.1109/10.855927. PubMed DOI

Beiranvand M., Shahbakhti M., Eslamizadeh M., Bavi M., Mohammadifar S. Investigating Wavelet Energy Vector for Pre-Term Labor Detection Using EHG Signals; Proceedings of the 2017 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA); Poznan, Poland. 22–24 September 2017; New York, NY, USA: IEEE; 2017. pp. 269–274. DOI

Taralunga D.D., Ungureanu M., Hurezeanu B., Gussi I., Strungaru R. Empirical Mode Decomposition Applied for Non-Invasive Electrohysterograhic Signals Denoising; Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Milan, Italy. 25–29 August 2015; New York, NY, USA: IEEE; 2015. pp. 4134–4137. PubMed DOI

Ren P., Yao S., Li J., Valdes-Sosa P.A., Kendrick K.M. Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals. PLoS ONE. 2015;10:e0132116. doi: 10.1371/journal.pone.0132116. PubMed DOI PMC

Chkeir A., Marque C., Terrien J., Karlsson B. Denoising Electrohysterogram via Empirical Mode Decomposition; Proceedings of the ISSNIP Biosignals. And Biorobotics Conference; Vitoria, Brazil. 4–6 January 2010; pp. 32–35.

Hassan M., Boudaoud S., Terrien J., Karlsson B., Marque C. Combination of Canonical Correlation Analysis and Empirical Mode Decomposition Applied to Denoising the Labor Electrohysterogram. IEEE Trans. Biomed. Eng. 2011;58:2441–2447. doi: 10.1109/TBME.2011.2151861. PubMed DOI

Hoseinzadeh S., Amirani M.C. Use of Electro Hysterogram (EHG) Signal to Diagnose Preterm Birth; Proceedings of the Electrical Engineering (ICEE), Iranian Conference; Mashhad, Iran. 8–10 May 2018; New York, NY, USA: IEEE; 2018. pp. 1477–1481. DOI