992
Views
22
CrossRef citations to date
0
Altmetric
Reviews

Developments in the human machine interface technologies and their applications: a review

& ORCID Icon
Pages 552-573 | Received 09 Jan 2021, Accepted 24 May 2021, Published online: 29 Jun 2021

References

  • Wanluk N, Visitsattapongse S, Juhong A, et al. Smart wheelchair based on eye tracking. 9th Biomedical Engineering International Conference (BMEiCON). Laung Prabang, Laos. 2016;1–4. DOI:https://doi.org/10.1109/bmeicon.2016.7859594
  • Lech MM, Hill TD, Arvidson AL, et al. Quality management system with human machine interface for industrial automation. United States patent US 6,539,271 B2. 2003 March 25.
  • Kim IS. Human reliability analysis in the man machine interface design review. Ann Nucl Energy. 2001;28(11):1069–1081.
  • Varela DT, Penaloza FG, Rodelas CJV. Characterized bioelectric signals by means of neural networks and wavelets to remotely control a human-machine interface. Sensors. 2019;19(8):1923.
  • Zoe S, Gabriel MN, Peter T, et al. Bioelectrical understanding and engineering of cell biology. J R Soc Interface. 2020;17:166.
  • Li A, Zhou J, Widelitz RB, et al. Integrating bioelectrical currents and Ca2+ signaling with biochemical signaling in development and pathogenesis. Bioelectricity. 2020;2(3):210–220.
  • Moore MM. Real-world applications for brain-computer interface technology. IEEE Trans Neural Syst Rehabil Eng. 2003;11(2):162–165.
  • Buzsaki G, Anastassiou CA, Koch C. The origin of extracellular fields and currents–EEG, ECoG, LFP and spikes. Nat Rev Neurosci. 2012;13(6):407–420.
  • Clark JW. The origin of biopotentials. In: Webster JG, editor. Medical instrumentation: application and design. 4th edition. John Wiley & Sons; 1998.
  • Vasileios CP, Themis PE, Dimitrios IF. Chapter 2 – types and sources of medical and other related data. In: Vasileios C. Pezoulas, Themis P. Exarchos, Dimitrios I. Fotiadis, editors. Medical data sharing, harmonization and analytics. Cambridge (MA): Academic Press; 2020. p. 19–65.
  • Atwood HL, Mackay WA. In essentials of neurophysiology. Hamilton: B.C. Decker 1989.
  • Farina D, Stegeman DF, Merletti R. Biophysics of the generation of EMG signals. In: R. Merletti, D. Farina editors. Surface electromyography: physiology, engineering, and applications. Hoboken (NJ): 2016.
  • Madiha T, Trivailo PM, Milan S. EEG-based BCI control schemes for lower-limb assistive-robots. Front Hum Neurosci. 2018;12:312.
  • Geethanjali P. Myoelectric control of prosthetic hands: state-of-the-art review. Med Devices. 2016;9:247–255.
  • Huang Q, He S, Wang Q, et al. An EOG-based human–machine interface for wheelchair control. IEEE Trans Biomed Eng. 2018;65(9):2023–2032.
  • Zhang J, Wang B, Zhang C, et al. An EEG/EMG/EOG-based multimodal human-machine interface to real-time control of a soft robot hand. Front Neurorob. 2019;13:7.
  • Shahin MK, Tharwat A, Gaber T, et al. A wheelchair control system using human-machine interaction: single-modal and multimodal approaches. J Intell Syst. 2019;28(1):115–132.
  • Dong B, Shi Q, Yang Y, et al. Technology evolution from self-powered sensors to AIoT enabled smart homes. Nano Energy. 2021;79:105414.
  • 3 Input/Output Technologies: Current Status and Research Needs. National Research Council. 1997. More Than Screen Deep: Toward Every-Citizen Interfaces to the Nation's Information Infrastructure. Washington, DC: The National Academies Press.
  • Garber L. Gestural technology: moving interfaces in a new direction [technology news]. Computer. 2013;46(10):22–25.
  • Lee Y, Kim J, Jang B, et al. Graphene-based stretchable/wearable self-powered touch sensor. Nano Energy. 2019;62:259–267.
  • Shi Q, Zhang Z, Chen T, et al. Minimalist and multi-functional human machine interface (HMI) using a flexible wearable triboelectric patch. Nano Energy. 2019;62:355–366.
  • Ferreira A, Celeste WC, Cheein FA, et al. Human-machine interfaces based on EMG and EEG applied to robotic systems. J Neuro-Eng Rehab. 2008;5:10.
  • Gyuton A, Hall J. Text book of medical physiology. Philadelphia: Elsevier Saunders; 2006.
  • Papanicolaou AC, Moore BD, Del‘Tsch G. Reorganization of cerebral function following lesions in the left hemisphere. In: Bach-y-rita P, editor. Traumatic brain injury. New York: Demos; 1989. p.105–119.
  • Kandel ER, Schwartz JH, Jessel TM. Principles of Neural Sciences. 4th ed. The Howard Hughes Medical Institute. New York (NY): McGraw-Hill Education/Medical; 2000.
  • Eysenck MW, Keane MT. Cognitive psychology: a student’s handbook. 6th ed. Hove & Newyork: Psychology Press. Taylor & Francis Group; 2010.
  • D’Mello AM, Gabrieli JDE, Nee DE. Evidence for hierarchical cognitive control in the human cerebellum. Curr Biol. 2020;30(10):1881–1892.e3.
  • Faugeras O, Adde G, Charpiat G, et al. Variational, geometric, and statistical methods for modeling brain anatomy and function. NeuroImage. 2004; 23:S46– S55.
  • Srivastava A, Kumari M, Gond DP, et al. Chapter 19 – basic overview of human physiology. In: Paul S, Bhatia D, editors. Smart healthcare for disease diagnosis and prevention. Cambridge (MA): Academic Press; 2020. p. 193–212.
  • Graimann B, Allison B, Pfurtscheller G. Brain–computer interfaces: a gentle introduction. In: Graimann B, Pfurtscheller G, Allison B, editors. Brain – computer interfaces. The frontiers collection. Heidelberg, Berlin: Springer; 2009.
  • Rabie AR, Athanasios VV. Brain computer interface: controls signals review. Neurocomputing. 2017;223:26–44.
  • Posner M, Petersen SE. The attention system of the human brain. Ann Rev Neurosci. 1990;13(1):25–42.
  • Bach-y-Rita P. Brain plasticity as a basis for recovery of functions in humans: recovery function: theoretical considerations for brain injury rehabilitation. Neuropsychologia. 1990;28(6):547–554.
  • Bach-y-Rita P. Recovery of function: theoretical considerations for brain injury rehabilitation. Bern, SW: Hans Huber; 1980.
  • Bach-y-Rita P. Brain plasticity. In: J GOODGOLD, editor. Rehabilitation medicine. St. Louis (MO):CV Mosby Co.; 1988. p.113–118.
  • Cotman CW, Sampedro MN. Progress in facilitating the recovery of function after central nervous system trauma. Ann NY Acad Sci. 1985;457(1 Hope for a Ne):83–104.
  • Finger S, LeVere TE, Almli CR, et al. Recovery of function. In: Finger S, Levere TE, Almli CR, Stein DG., editors. Brain injury and recovery. Boston. MA: Springer; 1988. p. 351–361.
  • Stein DG, Rosen JJ, Butters N., editors. Plasticity and recovery of function in the central nervous system. New York: Academic Press; 1974.
  • Sale A, Berardi N, Maffei L, at al. Environment and brain plasticity: towards an endogenous pharmacotherapy. Physiol Rev. 2014;94(1):189–234.
  • Kornorski J. The physiological approach to the problem of recent memory. In: Learning A Fessard, editor. Brain mechanisms. Oxford: Blackwell; 1961. p. 115–132.
  • Monday HR, Younts TJ, Castillo PE. Long-term plasticity of neurotransmitter release: emerging mechanisms and contributions to brain function and disease. Ann Rev Neurosci. 2018;41:299–322.
  • Golub MD, Chase SM, Batista AP, et al. Brain–computer interfaces for dissecting cognitive processes underlying sensorimotor control. Curr Opin Neurobiol. 2016;37:53–58.
  • Amiri S, Fazel-Rezai R, Asadpour V. A review of hybrid brain-computer interface systems. Adv Hum Comput Interact. 2013;2013:1–8.
  • Chumerin N, Manyakov NV, Vliet MV, et al. Pre-processing and decoding steady-state visual evoked potentials for brain-computer interfaces. Digital image and signal processing for measurement systems. Denmark: River Publishers; 2012. p. 1–33.
  • Nakanishi M, Wang Y, Wang Y-T, et al. A high-speed brain speller using steady-state visual evoked potentials. Int J Neural Syst. 2014;24(6):1450019.
  • Kübler A, Neumann N, Kaiser J, et al. Brain-computer communication: self-regulation of slow cortical potentials for verbal communication. Arch Phys Med Rehabil. 2001;82:1533–1539.
  • Padfield N, Zabalza J, Zhao H, et al. EEG-based brain-computer interfaces using motor-imagery: techniques and challenges. Sensors. 2019;19(6):1423.
  • Zhang W, Tan C, Sun F, et al. A review of EEG-based brain-computer interface systems design. Brain Sci Adv. 2018;4(2):156–167.
  • Li Z, Yuan Y, Luo L, et al. Hybrid brain/muscle signals powered wearable walking exoskeleton enhancing motor ability in climbing stairs activity. IEEE Trans Med Robot Bionics. 2019;1(4):218–227.
  • Choi I, Rhiu I, Lee Y, et al. A systematic review of hybrid brain-computer interfaces: taxonomy and usability perspectives. PLoS One. 2017;12(4):e0176674.
  • Jiang J, Zhou Z, Yin E, et al. Hybrid brain-computer interface (BCI) based on the EEG and EOG signals. Biomed Mater Eng. 2014;24(6):2919–2925.
  • Prashant P, Joshi A, Gandhi V. Brain computer interface: a review. 5th Nirma University International Conference on Engineering (NUiCONE). Ahmedabad. 2015. p. 1–6.
  • Cauvery NK, Lingaraju G, Anupama H. Brain-computer interface and its types-a study. Int J Adv Eng Technol. 2012; 3:739–745.
  • Millan Jose del R, Carmena JM. Invasive or non-invasive: understanding brain-machine interface technology. IEEE Eng Med Biol Mag. 2010;29:6–22.
  • Velliste M, Perel S, Spalding MC, et al. Cortical control of a prosthetic arm for self-feeding. Nature. 2008;453(7198):1098–1101.
  • Ganguly K, Carmena JM. Emergence of a stable cortical map for neuroprosthetic control. PLOS Biol. 2009;7:1–3.
  • Behrens E, Zentner J, van Roost D, et al. Subdural and depth electrodes in the presurgical evaluation of epilepsy. Acta Neurochirurgica. 1994;128:84–87.
  • Yadav MK, Verma U, Parikh H, et al. Minimally invasive transgingival implant therapy: a literature review. Natl J Maxillofac Surg. 2018;9(2):117.
  • Taussig D, Montavont A, Isnard J. Invasive EEG explorations. Clin Neurophysiol. 2015;45(1):113–119.
  • Kassiri JJ, Pugh J, Carline S. Depth electrodes in pediatric epilepsy surgery. The Canadian journal of neurological sciences. Le journal canadien des sciences neurologiques. 2013;40:48–55.
  • Shah AK, Mittal S. Invasive electroencephalography monitoring: indications and presurgical planning. Ann Indian Acad Neurol. 2014;17(Suppl 1):S89–S94.
  • Schalk G, Leuthardt EC. Brain-computer interfaces using electrocorticographic signals. IEEE Rev Biomed Eng. 2011;4:140–154.
  • Amanpour B, Erfanian A. Classification of brain signals associated with imagination of hand grasping, opening and reaching by means of wavelet-based common spatial pattern and mutual information. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Conference. 2013 Jul 3–7; Osaka, Japan: IEEE Engineering in Medicine and Biology Society; p. 2224–2227.
  • Pistohl T, Ball T, Schulze-Bonhage A, et al. Prediction of arm movement trajectories from ECoG-recordings in humans. J Neurosci Methods. 2008;167(1):105–114.
  • Villringer A, Chance B. Non-invasive optical spectroscopy and imaging of human brain function. Trends Neurosci. 1997;20:435–442.
  • Sornmo L, Laguna P. Bioelectrical signal processing in cardiac and neurological applications. U.S.A.: Elsevier Academic Press; 2005. p. 688.
  • Chi YM, Deiss SR, Cauwenberghs G. Non-contact low power EEG/ECG electrode for high density wearable biopotential sensor networks. Sixth International Workshop on Wearable and Implantable Body Sensor Networks. Berkeley, CA; 2009. p. 246–250.
  • Xu J, Yazicioglu RF, Van Hoof C, et al. An active electrode read out circuit. In: Low Power Active Electrode ICs for Wearable EEG Acquisition. Analog Circuits and Signal Processing. Cham: Springer; 2018. p. 125.
  • Gargiulo G, Bifulco P, Cesarelli M, et al. Problems in assessment of novel bio-potential front-end with dry electrode: a brief review. Machines. 2014;2:87–98.
  • Ball T, Kern M, Mutschler I, et al. Signal quality of simultaneously recorded invasive and non-invasive EEG. NeuroImage. 2009;46(3):708–716.
  • Okada Y. Neurogenesis of evoked magnetic fields. In: SJ Williamson, editor. Biomagnetism: an interdisciplinary approach. Springer; 1983. p. 399–408.
  • Ramadan RA, Refat S, Elshahed MA. Basics of brain computer interface. In: Hassanien A, Azar A., editors. Brain – computer interfaces. Intelligent systems reference library. Cham: Springer; 2015. p. 74.
  • Fouad MM, Amin KM, El-Bendary N, et al. Brain computer interface: a review. In: Hassanien A, Azar A, editors. Brain – computer interfaces. Vol. 74. Intelligent Systems Reference Library. Springer International Publishing; 2014. p. 3–30.
  • Kameswara T, Rajyalakshmi M, Prasad TV. An exploration on brain computer interface and its recent trends. IJARAI. Ithaca, New York; 2012;1(8):1.
  • del R. Milan J, Carmena J. Invasive or noninvasive: understanding brain-machine interface technology. IEEE Eng Med Biol Mag. 2010;29(1):16–22.
  • Blankertz B, Dornhege G, Lemm S. The Berlin brain – computer interface: machine learning based detection of user specific brain states. J Universal Comput Sci. 2006;2:581–607.
  • Millan J del R, Ferrez PW, Galan F. Non-invasive brain-machine interaction. Int J Pattern Recognit Artif Intell. 2008;22:959–972.
  • Meinicke P, Kapper M, Heumann M, et al. Improving transfer rates in brain computer interfacing: a case study. In: Becker S, Thrun S, Obermayer K, editors. Advances in neural information processing systems 15, MIT Press; 2003. p. 1107–1114.
  • Lance BJ, Kerick SE, Ries AJ, et al. Brain–computer interface technologies in the coming decades. Proceedings of the IEEE-13. May 2012 100:1585-1599. no. Special centennial issue.
  • Campisi P, La Rocca D. Brain waves for automatic biometric-based user recognition. IEEE Transinformforensic Secur. 2014;9(5):782–800.
  • Rohan H. Brain computer interface-controlling devices utilizing the alpha brain waves. Int J Sci Technol Res. 2015;4:281–285.
  • Merlo A, Farina D, Merletti R. A fast and reliable technique for muscle activity detection from surface EMG signals. IEEE Trans Biomed Eng. 2003;50(3):316–323.
  • Raurale S. Acquisition and processing real-time EMG signals for prosthesis active hand movements. International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE); March 6–8; Coimbatore, India; 2014. p. 1–6.
  • Saad I, Bais NH, Bun Seng C, et al. Electromyogram (EMG) signal processing analysis for clinical rehabilitation application. 2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation, Kota Kinabalu, Sabah, Malaysia; 2–4 December; 2015. p. 105–110.
  • Polisiero M, Bifulco P, Liccardo A, et al. Design and assessment of a low-cost, electromyographically controlled, prosthetic hand. Med Devices. 2013;6:97–110.
  • Gohel V, Mehendale N. Review on electromyography signal acquisition and processing. Biophys Rev. 2020;12(6):1361–1367.
  • Kavitha C, Nagappan G. Sensing and processing of EOG signals to control human machine interface system. Int J Sci Eng Technol Res. 2015;4(5):1330–1336.
  • Guo X, Pei W, Wang Y, et al. A human-machine interface based on single channel EOG and patchable sensor. Biomed Signal Process Control. 2016;30:98–105.
  • Salinet JL, Silva OL. Chapter 2 – ECG signal acquisition systems. In: Madeiro JPV, Cortez PC, Filho JMSM, Brayner ARA, editors. Developments and applications for ECG signal processing. Elsevier; 2019. p. 29–51.
  • Macfarlane PW, Oosterom AV, Pahlm O, et al. Comprehensive electrocardiology. Vol. 1. 2010. Verlag, London: Springer Science & Business Media.
  • Gao Z, Wu J, Zhou J, et al. Design of ECG signal acquisition and processing system. International Conference on Biomedical Engineering and Biotechnology, Macau, Macao; May 28–30. 2012. p. 762–764.
  • Funk M. As health care technology advances: benefits and risks. Am J Crit Care. 2011;20(4):285–291.
  • Sandberg F, Holmer M, Olde B, et al. Monitoring respiration using the pressure sensors in a dialysis machine. Physiol Meas. 2019;40(2):025001.
  • Johnson K, Pearce F, Westenskow D, et al. Clinical evaluation of the life support for trauma and transport (LSTAT) platform. Crit Care. 2002;6(5):439–446.
  • Sultan S, Mohan P. How to interact: evaluating the interface between mobile healthcare systems and the monitoring of blood sugar and blood pressure. 6th Annual International Mobile and Ubiquitous Systems: Networking & Services, MobiQuitous, Toronto, ON, Canada. 2009. p. 1–6.
  • Parati G, Stergiou GS, Asmar R, et al. On behalf of the ESH working group on blood pressure monitoring european society of hypertension guidelines for blood pressure monitoring at home: a summary report of the Second International Consensus Conference on home blood pressure monitoring. J Hypertension. 2008;26(8):1505–1526.
  • Kleinert HD, Harshfield GA, Pickering TG, et al. What is the value of home blood pressure measurement in patients with mild hypertension? Hypertension. 1984; 6:574–578.
  • Niiranen T, Rissanen H, Johansson J, et al. Overall cardiovascular prognosis of isolated systolic hypertension, isolated diastolic hypertension and pulse pressure defined with home measurements: the Finn-home study. J Hypertension. 2014;32(3):518–524.
  • Grant S, Blom AW, Craddock I, et al. Home health monitoring around the time of surgery: qualitative study of patients’ experiences before and after joint replacement. BMJ Open. 2019;9(12):e032205.
  • Chan M, Esteve D, Escriba C, et al. A review of smart homes – present state and future challenges. Comput Methods Programs Biomed. 2008;91(1):55–81.
  • Li R, Lu B, McDonald-Maier KD. Cognitive assisted living ambient system: a survey. Digit Commun Netw. 2015;1(4):229–252.
  • Ruhunage I, Perera CJ, Nisal K, et al. EMG signal controlled transhumerai prosthetic with EEG-SSVEP based approach for hand open/close. 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Banff, AB, Canada. 2017. 3169–3174.
  • Zhang X, Li R, Li H, et al. Novel approach for electromyography-controlled prostheses based on facial action. Med Biol Eng Comput. 2020;58:2685–2698.
  • Brammer M. The role of neuroimaging in diagnosis and personalized medicine-current position and likely future directions. Dialogues Clin Neurosci. 2009;11:389–396.
  • Fukaya N, Toyama S, Asfour T, et al. Design of the TUAT/Karlsruhe hand. Proceedings of the 2000 IEEE/RS. International Conference on Intelligent Robots and Systems. February 2000. 3: 1754–1759.
  • Asfour T, Berns K, Schelling J, et al. Programming of Manipulation Tasks of the Humanoid Robot ARMAR. The 9th International Conference on Advanced Robotics (ICAR’99). Tokyo, Japan. October 1999. p. 25–27.
  • Wei L, Hu H, Yuan K. Use of forehead bio-signals for controlling an intelligent wheelchair. Use of forehead bio-signals for controlling an intelligent wheelchair. 2008 IEEE International Conference on Robotics and Biomimetics, Bangkok. 2009. p. 108–113.
  • Heloyse UK, Fábio MA, Luciana SOT, et al. The Relationship between Electromyography and Muscle Force. EMG methods for evaluating muscle and nerve function. Editor-Mark Schwartz. Chapter-3. 2012.
  • Kamen G, Caldwell GE. Physiology and interpretation of the electromyogram. J Clin Neurophysiol. 1996;13:366–384.
  • Zou L, Ma X, Zhou T, et al. A study on surface EMG generation model and its extraction. 4th International Conference on Biomedical Engineering and Informatics (BMEI). Shanghai. 2011. p. 908–912.
  • Alarcao SM, Fonseca MJ. Emotions recognition using EEG signals: a survey. IEEE Transactions on Affective Computing. 2019 Jul 1–Sep 10. p. 374–393.
  • Ochoa JB. EEG signal classification for brain computer interface applications. January 2002.
  • Gao Y, Lee HJ, Mehmood RM. Deep learning of EEG signals for emotion recognition. IEEE International Conference on Multimedia & Expo Workshops (ICMEW). Turin, Italy; June 29–Jul 3;2015. p. 1–5.
  • Teplan M. Fundamental of EEG measurement. Measurement Science Review. 2002;2(2):1–11.
  • Kanwade AB, Gone RV, Ahire SJ, et al. Study of EOG signal generation, analyses, and acquisition system. Int Res J Eng Technol. 2017;4(4):3378–3382.
  • Merino M, Rivera O, Gomez I, et al. A method of EOG signal processing to detect the direction of eye movements. First International Conference on Sensor Device Technologies and Applications, Venice. 2010. p. 100–105.
  • Dhillon HS, Singla R, Rekhi NS, et al. EOG and EMG based virtual keyboard: a brain-computer interface. 2nd IEEE International Conference on Computer Science and Information Technology, Beijing, China; 2009. p. 259–262.
  • Gacek A, Pedrycz W. ECG signal processing, classification and interpretation: a comprehensive framework of computational intelligence. London: Springer-Verlag; 2012.
  • Wang Y, Yan J, Wen J, et al. An intracranial electroencephalography (iEEG) brain function mapping tool with an application to epilepsy surgery evaluation. Front Neuroinf. 2016; 10:15. DOI:https://doi.org/10.3389/fninf.2016.00015
  • Yang T, Hakimian S, Schwartz TH. Intraoperative electrocorticography (ECog): indications, techniques, and utility in epilepsy surgery. Epileptic Disord. 2014;16(3):271–279.
  • Namdev K, Siddiqui MM. Different types of electrical signals produced by human body. Int J Adv Res Sci Eng. 2015;4(Special Issue 02):232–239.
  • Parák J, Havlík J. ECG signal processing and heart rate frequency detection methods. Conference: Technical Computing 2011, Prague, Czech Republic; 2011.
  • Israel SA, Irvine JM, Andrew C, et al. ECG to identify individuals. Pattern Recognit. 2005;38(1):133–142.
  • Darvas F, Pantazis D, Kucukaltun-Yildirim E, et al. Mapping human brain function with MEG and EEG: methods and validation. NeuroImage. 2004;23:S289–S299.
  • Islam MA, Sundaraj K, Ahmad RB, et al. Mechanomyogram for muscle function assessment: a review. PLoS One. 2013;8(3):e58902.
  • Irfan MR, Sudharshan N, Santhanakrishan S, et al. A comparative study of EMG and MMG signals for practical application. International Conference on Signal, Image Processing and Applications with workshop of ICEEA. 2011. 21:p. 106–110.
  • Uhlhaas PJ, Liddle P, Linden DEJ, et al. Magnetoencephalography as a tool in psychiatric research: current status and perspective. Biol Psychiatr Cogn Neurosci Neuroimag. 2017;2(3):235–244.
  • Hari R, Salmelin R. Magnetoencephalography: from SQUIDs to neuroscience: neuroimage 20th anniversary special edition. NeuroImage. 2012;61:386–396.
  • Sharma M, Kacker S, Sharma M. A brief introduction and review on galvanic skin response. IJMRP. 2016;2(6):13–17.
  • Villarejo MV, Zapirain BG, Zorrilla AM. A stress sensor based on galvanic skin response (GSR) controlled by ZigBee. Sensors. 2012;12:6075–6101.
  • Vahey R, Becerra R. Galvanic skin response in mood disorders: a critical review. Int J Psychol Psychol Ther. 2015;15:275–304.
  • Purkayastha SS, Jain VK, Sardana HK. Topical review: a review of various techniques used for measuring brain activity in brain computer interfaces. Adv Electron Electric Eng. 2014;4:513–522.
  • Xue G, Chen C, Lu Z. Brain imaging techniques and their applications in decision-making research. Xin li xue bao. Acta psychologica Sinica. 2010;42:120–137.
  • Piston emission tomography [Online] Available from: https://www.hopkinsmedicine.org/healthlibrary/test_procedures/neurological/positron_emission_tomography_pet_92. p. 07654.
  • Coyle S, Ward T, Markham C, et al. On the suitability of near-infrared (NIR) systems for next-generation brain–computer interfaces. Physiol Meas. 2004;25:815–822.
  • Song J, Young BM, Nigogosyan Z, et al. Characterizing relationships of DTI, fMRI, and motor recovery in stroke rehabilitation utilizing brain-computer interface technology. Front Neuroeng. 2014;7:31.
  • Moerman A, Wouters P. Near-infrared spectroscopy (NIRS) monitoring in contemporary anaesthesia and critical care. Acta Anaesthesiologica Belgica. 2010;61(4):185–194.
  • Rahmim A, Zaidi H. PET versus SPECT: strengths, limitations and challenges. Nucl Med Commun. March 2008;29(3):193–207.
  • Malmivuo J. Biomagnetism. Wiley Encyclopedia of Electrical and Electronics Engineering. 2017. p. 1–25..
  • Khalil SF, Mohktar MS, Ibrahim F. The theory and fundamentals of bioimpedance analysis in clinical status monitoring and diagnosis of diseases. Sensors. 2014;14(6):10895–10928.
  • Wang H, Ma X, Hao Y. Electronic devices for human-machine interfaces. Adv Mater Interfaces. 2017;4(4):1600709.
  • Wang G, Li L, Xing S, et al. Intelligent HMI in orthopedic navigation. In: Zheng G, Tian W, Zhuang X, editors. Intelligent orthopaedics. Advances in experimental medicine and biology. Vol. 1093. Singapore: Springer; 2018. p. 207–224.
  • Zhang X, Chen G, Liao H. High-quality see-through surgical guidance system using enhanced 3-D autostereoscopic augmented reality. IEEE Transact Biomed Eng. 2017;64(8):1815–1825.
  • Zhang X, Li R, Li Y. Research on brain control prosthetic hand. 11th International Conference on Ubiquitous Robots and Ambient Intelligence. Kuala Lumpur. 2014. p. 554–557.
  • Merrill D, Lockhart J, Troyk PR, et al. Development of an implantable myoelectric sensor for advanced prosthesis control. Artif Organs. 2011;35:249–252.
  • Cheesborough JE, Smith LH, Kuiken TA, et al. Targeted muscle reinnervation and advanced prosthetic arms. Seminars Plastic Surg. 2015;29(1):62–72.
  • Osborn LE, Iskarous MM, Thakor NV, et al. Chapter 22 – sensing and control for prosthetic hands in clinical and research applications. In: Rosen J, Ferguson PW, editors. Wearable robotics. Elsevier; 2020. p. 445–468. DOI:https://doi.org/10.1016/C2017-0-01139-4
  • Su Y, Fisher MH, Wolczowski A, et al. Towards an EMG-controlled prosthetic hand using a 3-D electromagnetic positioning system. IEEE Trans Instrum Meas. 2017;56:178–186.
  • Belter JT, Segil JL, Dollar AM, et al. Mechanical design and performance specifications of anthropomorphic prosthetic hands: a review. J Rehab Res Dev. 2013;50(5):599–618.
  • Kyberd PJ, Chappell PH. The Southampton hand: an intelligent myoelectric prosthesis. J Rehab Res Dev. 1994;31(4):326–334.
  • Liu H, Xu K, Siciliano B, et al. The MERO hand: a mechanically robust anthropomorphic prosthetic hand using novel compliant rolling contact joint. IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Hong Kong, China. 2019. p. 126–132.
  • Ting Z, Wang XQ, Jiang L, et al. Biomechatronic design and control of an anthropomorphic artificial hand for prosthetic applications. Robotica. 2015;34(10):2291–2308.
  • Manfredo A, Matteo C, Henning M, et al. Deep learning with convolutional neural networks applied to electromyography data: a resource for the classification of movements for prosthetic hands. Front Neurorob. 2016;10:9.
  • Wang N, Lao K, Zhang X. Design and myoelectric control of an anthropomorphic prosthetic hand. J Bionic Eng. 2017;14(1):47–59.
  • Brunner I, Skouen JS, Hofstad H. Virtual reality training for upper extremity in subacute stroke (VIRTUES): a multicenter RCT. Neurology. 2017;89(24):2413–2421.
  • Perry BN, Armiger RS, Yu KE, et al. Virtual integration environment as an advanced prosthetic limb training platform. Front Neurol. 2018;9:785.
  • Bright D, Nair A, Salvekar D, et al. EEG-based brain controlled prosthetic arm. Conference on Advances in Signal Processing (CASP). Pune. 2016. p. 479–483.
  • Andrecioli R, Engeberg E. Adaptive sliding manifold slope via grasped object stiffness detection with a prosthetic hand. Mechatronics. 2013;23(8):1171–1179.
  • Wang Y, Liu H, Leng D, et al. New advances in EMG control methods of anthropomorphic prosthetic hand. Sci China Technol Sci. 2017;60(12):1978–1979.
  • Reaz M, Hussain M, Mohd-Yasin F. Techniques of EMG signal analysis: detection, processing, classification and application. Biol Proc. 2006;8:11–35.
  • Jiang N, Dosen S, Muller K, et al. Myoelectric control of artificial limbs—is there a need to change focus? [In the Spotlight]. IEEE Signal Proc Mag. Sep. 2012;29:152.
  • Krasoulis A, Kyranou I, Erden MS, et al. Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements. J Neuro-Eng Rehab. 2017;14:71.
  • Olivieri E, Barresi G, Leonardo SM. BCI-based user training in surgical robotics. Conference proceedings: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Milano, Italy; 2015. p. 4918–4921.
  • Hart SG, Staveland LE. Development of nasa-tlx (task load index): results of empirical and theoretical research, human mental workload. Vol. 52. Hancock PA, Meshkati N, editors. North-Holland, Amsterdam; 1988. p. 139–183.
  • Gallagher AG, Satava RM, Osullivan GC. Attentional capacity: an essential aspect of surgeon performance. Ann Surg. 2015;261(3):e60–e61.
  • Zander T, Shetty K, Lorenz R, et al. Automated task load detection with electroencephalography: towards passive brain–computer interfacing in robotic surgery. J Med Robot Res. 2017;02(01):1750003.
  • Kristjansson A, Johannesson O, Mitrut O, et al. Designing sensory-substitution devices: principles, pitfalls and potential. Restor Neurol Neurosci. 2016;34(5):769–787.
  • On Visell. Tactile sensory substitution: models for enaction in HCI. Interact Comput. 2009;21(1–2):38–53.
  • Lenay C, Olivier G, Sylvain H, et al. Sensory substitution: limits and perspectives. In: Hatwell Y, Streri A, Gentaz E, editors. Advances in consciousness research. Touching for knowing: cognitive psychology of haptic manual perception. Amsterdam (The Netherlands): John Benjamins Publishing Company; 2003. p. 275–292.
  • Paterson M. Molyneux, neuroplasticity, and technologies of sensory substitution. In: Glenney B, Silva JF, editors. The senses and the history of philosophy. Routledge; 2019. p. 340–352.
  • Bach-y-Rita P, Collins C, Saunders F, et al. Vision substitution by tactile image projection. Nature. 1969;221(5184):963–964.
  • Bach-y-Rita P, Kercel SW. Sensory substitution and the human–machine interface. Trends Cogn Sci. 2003;7(12):541–546.
  • Capelle C, Trullemans C, Arno P, et al. A real-time experimental prototype for enhancement of vision rehabilitation using auditory substitution. IEEE Trans Biomed Eng. 1998;45:1279–1293.
  • Egilmez OK, Kalcioglu MT. Cochlear implant: indications, contraindications and complications. SSM. 2015;47(4):9–28.
  • Tyler M, Danilov Y, Bach-y-Rita P, et al. Closing an open-loop control system: vestibular substitution through the tongue. J IntegrNeurosci. 2003;2:159–164.
  • Gori M, Cappagli G, Tonelli A, et al. Devices for visually impaired people: high technological devices with low user acceptance and no adaptability for children. Neurosci Biobehav Rev. 2016;69:79–88.
  • Steven AW, Stephen B. Sensory substitution using tactile pin arrays: human factors, technology and applications. Signal Process. 2006;86(12):3674–3695.
  • Collinger JL, Foldes S, Bruns TM, et al. Neuroprosthetic technology for individuals with spinal cord injury. J Spinal Cord Med. 2013;36(4):258–272.
  • Noor NMM, Ahmad S. Analysis of different EOG-based eye movement strength levels for wheelchair control. IJBET. 2013;11(2):175–196.
  • Millan JR, Renkens F, Mourino J. Non-invasive brain-actuated control of a mobile robot by human EEG. IEEE Trans Biomed Eng. 2004;51:1026–1033.
  • Turnip A, Soetraprawata D, Turnip M, et al. EEG-based brain-controlled wheelchair with four different stimuli frequencies. Internetwork Indonesia J 2016;8:65–69.
  • Geng T, Gan JQ, Hu H. A self-paced online BCI for mobile robot control. Internat J Advance Mechatron Sys. 2010;2(1/2):28–35.
  • Geng T, Gan JQ. Motor prediction in brain–computer interfaces for controlling mobile robots. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vancouver. BC. Canada. 2008. p. 634–637.
  • Wei L, Hu H. A hybrid human-machine interface for hands-free control of an intelligent wheelchair. IJMA. 2011;1(2):97–111.
  • Strausser KA, Kazerooni H. The development and testing of a human machine interface for a mobile medical exoskeleton. IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA. 2011. p. 4911–4916.
  • World Health Organization. 2018. Global status report on road safety 2018: summary. World Health Organization.
  • Dingus TA, Klauser SG, Neale VL, et al. 100-Car naturalistic driving study – phase II – results of the 100-car field experiment. Report No. DOT HS8 10593: Virginia Tech. Transportation Institute – sponsored by National Highway Traffic Safety Administration; 2006.
  • Pickering CA, Bumnham KJ, Richardson MJ. A review of automotive human machine interface technologies and techniques to reduce driver distraction. 2nd Institution of Engineering and Technology International Conference on System Safety. London. 2007. p. 223–228.
  • Carsten O, Martens MH. How can humans understand their automated cars? HMI principles, problems and solutions. Cogn Tech Work. 2019;21(1):3–20.
  • Ohn-Bar E, Trivedi MM. Hand gesture recognition in real time for automotive interfaces: a multimodal vision-based approach and evaluations. IEEE Trans Intell Transport Syst. 2014;15(6):2368–2377.
  • Lee FCH, Chan AHS. Attending visual and auditory signals: ergonomics recommendations with consideration of signal modality and spatial stimulus–response (S–R) compatibility. Int J Ind Ergon. 2007;37(3):197–206.
  • Brebner JMT, Welford AT. Introduction: an historical background sketch. In: Welford, AT, editor. Reaction times. New York: Academic Press;. 1980. p. 1–23.
  • Chan AHS, Chan KWL. Synchronous and asynchronous presentations of auditory and visual signals: implications for control console design. Appl Ergon. 2006;37(2):131–140.
  • Clarke JR, Clarke PM Sr; Sleep detection and driver alert apparatus. U.S. Patent No. 5689241. Nov. 18, 1997.
  • Lal KLS, Craig A, Boord P, et al. Development of an algorithm for an EEG-based driver fatigue countermeasure. J Saf Res. 2003;34(3):321–328.
  • Leavitt L. Sleep-detecting driving gloves. U.S. Patent No. 6016103. Jan 2000.
  • Bergasa LM, Nuevo J, Sotelo MA, et al. Real-time system for monitoring driver vigilance. IEEE Trans Intell Transport Syst. 2006;7(1):63–77.
  • Liu J-X, Ko M-K. Detection of driver’s low vigilance using vehicle steering information and facial inattention features, in Proc. 20th ITS World Congress. Tokyo, Japan. 2013. p. 2066–2075.
  • Sigari MH, Fathy M, Soryani M. A driver face monitoring system for fatigue and distraction detection. Int J Vehicular Technol. 2013;2013:1–100.
  • Giusti A, Zocchi C, Rovetta A. A noninvasive system for evaluating driver vigilance level examining both physiological and mechanical data. IEEE Trans Intell Transport Syst. 2009;10(1):127–134.
  • Papcun P, Kajáti E, Koziorek J. Human Machine Interface in Concept of Industry 4.0, 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA), Košice, Slovakia. 2018. p. 289–296.
  • Lu Y. Industry 4.0. A survey on technologies, applications and open research issues. J Ind Inf Integration. 2017;16:1–10.
  • Nahavandi S. Industry 5.0 – a human-centric solution. Sustainability. 2019;11(16):4371.
  • Chan A, Or CK. A comparison of semantic and spatial stimulus-response compatibility effects for human-machine interface design. EJIE. 2012;6(5):629–643.
  • Mulder. Cybernetics of Tunnel-in-the-Sky Displays. Ph.D. Thesis, delft University of Technology. Faculty of Aerospace. 1999.
  • Abeloos ALM, Paassen MMV, Marinus M. The applicability of an adaptive human-machine interface in the cockpit. Conference on human decision making and manual control. Ispra. June 26–28, 2000.
  • Bian Y, Zhao L, Li H, et al. Research on multi-modal human-machine interface for aerospace robot. 7th International Conference on Intelligent Human-Machine Systems and Cybernetics. Hangzhou, 2015.p. 535–538.
  • Smith CJ. Design of the Eurofighter human machine interface, air & space Europe. May–June 1999;1:54–59.
  • Lim Y, Ramasamy S, Gardi A, et al. Cognitive human-machine interfaces and interactions for unmanned aircraft. J Intell Robot Syst. 2018;91(3–4):755–745.
  • Gohmert DM. Seating considerations for spaceflight: the human to machine interface. Proceedings of the 5th IAASS Conference. A Safer Space for Safer World by Ouwehand L Noordwijk, Netherlands: European Space Agency. January 2012. p. 20.
  • Heymann M, Degani A. Constructing human-automation interfaces: a formal approach. Ninth International Conference on Human-Computer Interaction in Aeronautics. Massachusetts Institute of Technology. Boston. 2002.
  • Lim Y, Ranasinghe K, Gardi A, et al. Human-machine interfaces and interactions for multi UAS operations. Proceedings of the 31th Congress of the International Council of the Aeronautical Sciences (ICAS 2018). Belo Horizonte, Brazil. September 2018.
  • Foster TG. Human machine interface programming and testing. NASA USRP – Internship Final Report. Kennedy Space Center. Major: Computer Engineering. 29 July 2013.
  • Zhang D, Song F, Xu Y, et al. Advanced pattern recognition technologies with applications to biometrics. Hershey: Medical Information Science Reference. 2009.
  • Pankanti S, Bolle RM, Jain A. Biometrics: the future of identification [Guest editors’ introduction]. Computer. 2000;33(2):46–49.
  • Marohn D. Biometrics in healthcare. Biom Technol Today. 2006;14(9):9–11.
  • Li SZ, editor. Encyclopedia of biometrics. Springer, US. 2009.
  • Ratha NK, Senior A, Bolle RM. Automated biometrics. In: Singh S, Murshed N, Kropatsch W, editors. Advances in pattern recognition – ICAPR 2001. Lecture notes in computer science. Vol. 2013. Heidelberg, Berlin: Springer; 2001. p. 447–455.
  • Jain AK, Flynn P, Ross AA. Handbook of biometrics. Switzerland: Springer Nature, 2007. p. 556.
  • Ortega-Garcia J, Bigun J, Reynolds D, et al. Authentication gets personal with biometrics. IEEE Signal Process Mag. March 2004;21(2):50–62.
  • Wayman JL. Fundamentals of biometric authentication technologies. Int J Image Grap. 2001;01(01):93–113.
  • Jain AK, Ross A, Prabhakar S. An introduction to biometric recognition. IEEE Trans Circuits Syst Video Technol. 2004;14(1):4–20.
  • Im SK, Park HM, Kim YW, et al. A biometric identification system by extracting hand vein patterns. J Korean Phys Soc. 2001;38:268–272.
  • Jain AK, Pankanti S, Prabhakar S, et al. Biometrics: a grand challenge. Proc 17th Int Conference on Pattern Recog. Cambridge, UK. 2004;2:935–942.
  • Page A, Kulkarni A, Mohsenin T. Utilizing deep neural nets for an embedded ECG-based biometric authentication system. IEEE Biomedical Circuits and Systems Conference (BioCAS), Atlanta, GA. 2015. p. 1–4.
  • Bharadi V, Pandya B, Nemade B. Multimodal biometric recognition using iris amp; fingerprint: by texture feature extraction using hybrid wavelets. 5th International Conference - Confluence The Next Generation Information Technology Summit 5th Sep 2014. p. 697–702. DOI:https://doi.org/10.1109/CONFLUENCE.2014.6949309
  • Kumar S, Ansari MD, Naik MV, et al. A comparative case study on machine learning based multi-biometric systems. In: Gunjan V, Senatore S, Kumar A, et al. editors. Advances in cybernetics, cognition, and machine learning for communication technologies. Lecture Notes in Electrical Engineering. Vol. 643. Singapore: Springer;2020. p. 353–365. DOI:https://doi.org/10.1007/978-981-15-3125-5_36
  • Uludag U, Pankanti S, Prabhakar S, et al. Biometric cryptosystems: issues and challenges. Proc IEEE. 2004; 92(6):948–960.
  • Lanitis A. A survey of the effects of aging on biometric identity verification. IJBM. 2010;2(1):34.
  • Singh YN, Singh SK, Ray AK. Bioelectrical signals as emerging biometrics: issues and challenges. Int Scholar Res Notices. 2012;2012:1–13.
  • Pal A, Gautam AK, Singh YN. Evaluation of bioelectric signals for human recognition. Procedia Comput Sci. 2015;48:746–752.
  • Weinstein C. Military and Government Applications of Human-Machine Communication by Voice. Proc National Acad Sci USA. 1995 [cited 2021 Jan 6];92:10011–10016. .
  • Cupples EJ, Beek B. Proceedings of the NATO/AGARD Lecture Series No. 170, Speech Analysis and Synthesis and Man-Machine Speech Communications for Air Operations. 1990.
  • Kang GS, Fransen LJ. Speech analysis and synthesis based on pitch-synchronous segmentation of the speech waveform. Washington, DC: Naval Research Laboratories; 1994.
  • Ramasubramanian V, Doddala H. Introduction. In: Ultra low bit-rate speech coding. Springer briefs in electrical and computer engineering. New York: Springer; 2015.
  • Corazza A, De Mori R, Gretter R, et al. Language models for automatic speech recognition. In: Ayuso AJR, Soler JML, editors. Speech recognition and coding. NATO ASI series (series F: computer and systems sciences). Heidelberg, Berlin: Springer; 1995. p. 147.
  • Crescentini C, Fabbro F, Tomasino B. Editorial special topic: enhancing brain and cognition through meditation. J Cogn Enhanc. 2017;1(2):81–83.
  • Wilms W, Woźniak-Karczewska M, Corvini PF-X, et al. Nootropic drugs: methylphenidate, modafinil and piracetam – population use trends, occurrence in the environment, ecotoxicity and removal methods – a review. Chemosphere. 2019;233:771–785.
  • Morton WA, Stockton GG. Methylphenidate abuse and psychiatric side effects. Prim Care Companion J Clin Psychiatry. 2000;2(5):159–164.
  • Kim D. Practical use and risk of modafinil, a novel waking drug. Environ Health Toxicol. 2012;27:e2012007.
  • Malykh AG, Sadaie MR. Piracetam and piracetam-like drugs. Drugs. 2010;70:287–312.
  • Colucci L, Bosco M, Rosario Ziello A, et al. Effectiveness of nootropic drugs with cholinergic activity in treatment of cognitive deficit: a review. J Exp Pharmacol. 2012;4:163–172.
  • Dan-Dan L, Ya-Hon Z, Wei Z, et al. Meta-analysis of randomized controlled trials on the efficacy and safety of donepezil, galantamine, rivastigmine, and memantine for the treatment of Alzheimer’s disease. Front Neurosci. 2019;13:472.
  • Santoro A, Siviero P, Minicuci N, et al. Effects of donepezil, galantamine and rivastigmine in 938 Italian patients with Alzheimer’s disease: a prospective, observational study. CNS Drugs. 2010;24(2):163–176.
  • Yesavage JA, Mumenthaler MS, Taylor JL, et al. Donepezil and flight simulator performance: effects on retention of complex skills. Neurology. 2002;59(1):123–123.
  • Caldwell JA, Caldwell JL, Smyth NK, et al. A double-blind, placebo-controlled investigation of the efficacy of modafinil for sustaining the alertness and performance of aviators: a helicopter simulator study. Psychopharmacology. 2000;150:272–282.
  • Meng J, Mundahi JH, Streitz TD, et al. Effects of soft drinks on resting state EEG and brain–computer interface performance. IEEE Access. 2017;5:18756–18764.
  • Teter CJ, McCabe SE, Lagrange K, et al. Illicit use of specific prescription stimulants among college students: prevalence, motives, and routes of administration. Pharmacotherapy. 2006;26:1501–1510.
  • Sattler S, Forlini C, Racine E, et al. Impact of contextual factors and substance characteristics on perspectives toward cognitive enhancement. PLOS One. 2013;8(8):e71452.
  • Vassanelli S. Brain-chip interfaces: the present and the future. Procedia Comput Sci. 2011;7:61–64.
  • Indiveri G, Barranco BL, Legenstein R, et al. Integration of nanoscale memristor synapses in neuromorphic computing architectures. Nanotechnology. 2013;24(38):1–13.
  • John V, Kondziolka DS. Device for multicentric brain modulation, repair and interface. U.S. Patent No. 2008/0154331 A1. June 26, 2008.
  • Vassanelli S, Mahmud M, Girardi S, at al. On the way to large-scale and high-resolution brain-chip interfacing. Cogn Comput. 2012;4(1):71–81.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.