EMG-based and gaze-tracking-based man-machine interfaces.

A great demand for brain-machine and, more generally, man-machine interfaces is arising nowadays, pushed by several promising scientific and technological results, which are encouraging the concentration of efforts in this field. The possibility of measuring, processing and decoding brain activity, so as to interpret neural signals, is often looked at as a possibility to bypass lost or damaged neural and/or motor structures. Beyond that, such interfaces currently show a potential for applications in other fields, space science being certainly one of them. At present, the concept of "reading" the brain to detect intended actions and use these to control external devices is being studied with several technical and methodological approaches; among these, interfaces based on electroencephalographic signals play today a prominent role. Within such a context, the aim of this section is to present a brief survey on two types of noninvasive man-machine interfaces based on a different approach. In particular, they rely on the extraction of control signals from the user with techniques that adopt electromyography and gaze tracking. Working principles, implementations, typical features, and applications of these two types of interfaces are reported.

[1]  Cristina Conati,et al.  Eye-tracking for user modeling in exploratory learning environments: An empirical evaluation , 2007, Knowl. Based Syst..

[2]  Arie E. Kaufman,et al.  An eye tracking computer user interface , 1993, Proceedings of 1993 IEEE Research Properties in Virtual Reality Symposium.

[3]  Yoky Matsuoka,et al.  An EMG-Controlled Hand Exoskeleton for Natural Pinching , 2004, J. Robotics Mechatronics.

[4]  Daniel Thalmann,et al.  Coding gaze tracking data with chromatic gradients for VR Exposure Therapy , 2007 .

[5]  R.Fff. Weir,et al.  A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  Agnieszka Bojko,et al.  Using eye tracking to compare web page designs: a case study , 2006 .

[7]  Enrico Costanza,et al.  EMG as a Subtle Input Interface for Mobile Computing , 2004, Mobile HCI.

[8]  G. Gini,et al.  An EMG-controlled exoskeleton for hand rehabilitation , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[9]  Kaj-Åge Henneberg,et al.  Principles of Electromyography , 1999 .

[10]  Roel Vertegaal,et al.  Designing attentive interfaces , 2002, ETRA.

[11]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[12]  Toshio Tsuji,et al.  A human-assisting manipulator teleoperated by EMG signals and arm motions , 2003, IEEE Trans. Robotics Autom..

[13]  F. K. Lam,et al.  Fuzzy EMG classification for prosthesis control. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[14]  Andrew T. Duchowski,et al.  Eye Tracking Methodology: Theory and Practice , 2003, Springer London.

[15]  Andreas Stainer-Hochgatterer,et al.  EOG Pattern Recognition Trial for a Human Computer Interface , 2007, HCI.

[16]  A. Guyton,et al.  Textbook of Medical Physiology , 1961 .

[17]  Carlos Hitoshi Morimoto,et al.  Eye gaze tracking techniques for interactive applications , 2005, Comput. Vis. Image Underst..

[18]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[19]  Robert Plonsey,et al.  Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields , 1995 .

[20]  G. Norris,et al.  The Eye Mouse, an eye communication device , 1997, Proceedings of the IEEE 23rd Northeast Bioengineering Conference.

[21]  Zhiwei Zhu,et al.  Eye and gaze tracking for interactive graphic display , 2002, SMARTGRAPH '02.

[22]  R. Benjamin Knapp,et al.  Towards an EOG-based eye tracker for computer control , 1998, Assets '98.

[23]  Carlo J. De Luca,et al.  The Use of Surface Electromyography in Biomechanics , 1997 .

[24]  Roberto Merletti,et al.  The extraction of neural strategies from the surface EMG. , 2004, Journal of applied physiology.

[25]  Simon Ferguson,et al.  Grasp Recognition From Myoelectric Signals , 2002 .

[26]  Mark Shelhamer,et al.  A new "wireless" search-coil system , 2008, ETRA.

[27]  Rajesh P. N. Rao,et al.  Real-Time Classification of Electromyographic Signals for Robotic Control , 2005, AAAI.

[28]  J. Webster Encyclopedia of Medical Devices and Instrumentation , 1988 .

[29]  Worthy N. Martin,et al.  Human-computer interaction using eye-gaze input , 1989, IEEE Trans. Syst. Man Cybern..

[30]  Shumin Zhai,et al.  What's in the eyes for attentive input , 2003, Commun. ACM.

[31]  Patrick van der Smagt,et al.  Surface EMG in advanced hand prosthetics , 2008, Biological Cybernetics.

[32]  Yoky Matsuoka,et al.  Comparison of control strategies for an EMG controlled orthotic exoskeleton for the hand , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[33]  Joseph D. Bronzino,et al.  The Biomedical Engineering Handbook , 1995 .

[34]  C. D. De Luca,et al.  Surface myoelectric signal cross-talk among muscles of the leg. , 1988, Electroencephalography and clinical neurophysiology.

[35]  S Micera,et al.  An algorithm for detecting the onset of muscle contraction by EMG signal processing. , 1998, Medical engineering & physics.

[36]  Stefano Ramat,et al.  Influence of orientation of exiting wire of search coil annulus on torsion after saccades. , 2004, Investigative ophthalmology & visual science.

[37]  Myung Jin Chung,et al.  Non-contact eye gaze tracking system by mapping of corneal reflections , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[38]  Jacob Rosen,et al.  A myosignal-based powered exoskeleton system , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[39]  John G. Webster,et al.  Medical Instrumentation: Application and Design , 1997 .

[40]  R.J.K. Jacob,et al.  Hot topics-eye-gaze computer interfaces: what you look at is what you get , 1993, Computer.

[41]  D. Robinson,et al.  A METHOD OF MEASURING EYE MOVEMENT USING A SCLERAL SEARCH COIL IN A MAGNETIC FIELD. , 1963, IEEE transactions on bio-medical engineering.

[42]  H. Collewijn,et al.  Precise recording of human eye movements , 1975, Vision Research.

[43]  Silvestro Micera,et al.  A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems , 2005, Journal of the peripheral nervous system : JPNS.

[44]  J. P. H. Reulen,et al.  Precise recording of eye movement: the IRIS technique Part 1 , 2006, Medical and Biological Engineering and Computing.

[45]  Patrick van der Smagt,et al.  Learning EMG control of a robotic hand: towards active prostheses , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[46]  Alcimar Soares,et al.  The Development of a Virtual Myoelectric Prosthesis Controlled by an EMG Pattern Recognition System Based on Neural Networks , 2004, Journal of Intelligent Information Systems.

[47]  Qiang Ji,et al.  Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance , 2002, Real Time Imaging.

[48]  S Micera,et al.  Improving detection of muscle activation intervals. , 2001, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.