Download PDF
Methods Inf Med 2008; 47(03): 192-197
DOI: 10.3414/ME9109
Original Article
Schattauer GmbH

Electrode Position Optimization for Facial EMG Measurements for Human-computer Interface

N. Nöjd
1   Department of Biomedical Engineering, Tampere University of Technology, Tampere, Finland
,
M. Hannula
1   Department of Biomedical Engineering, Tampere University of Technology, Tampere, Finland
,
N. Narra
1   Department of Biomedical Engineering, Tampere University of Technology, Tampere, Finland
,
J. Hyttinen
1   Department of Biomedical Engineering, Tampere University of Technology, Tampere, Finland
› Author Affiliations
Further Information

Publication History





Publication Date:
18 January 2018 (online)

    Permissions and Reprints

Summary

Objectives: The aim of this work was to model facial electromyography (fEMG) to find optimal electrode positions for wearable wireless human-computer interface. The measurement system is a head cap developed in our institute and with it we can measure fEMG and electro-oculogram (EOG). The signals could be used to control the computer interface: gaze directions move the cursor and muscle activations correspond to clicking.

Methods: A very accurate 3D model of the human head was developed and it was used in the modeling of fEMG. The optimal positions of four electrodes on the forehead measuring the activations of frontalis and corrugator muscles were defined. Calculations were based on reciprocity theorem and lead field concept.

Results: A new accurate model is now in our use for modeling purposes. It has high spatial accuracy and number of inhomogeneities providing a good platform for various simulations. The best measurement sensitivity is achieved by placing the electrodes parallel to the muscle cells. Anyway, better separating capability for frontalis and corrugator activation is achieved by placing the electrodes more orthogonally.

Conclusions: The developed model and the tools utilized are powerful methods to optimize the electrode positions of a wearable gaze and EMG-based user interface system. The modeling results provide direct feedback for developing next generation wearable head cap with optimized electrode locations.

Keywords

Human-computer interface - f EMG - modeling - electrode positions
 

  • References

  • 1 Nöjd N, Puurtinen M, Niemenlehto P, Vehkaoja A, Verho J, Vanhala T, Hyttinen J, Juhola M, Lekkala J, Surakka V. Wireless wearable EMG and EOG measurement system for psychophysiological applications. In: Lundström R. et al., editors. Proceedings of the 13th Nordic Baltic Conference on Biomedical Engineering and Medical Physics; 2005 June 13-17; Umeå, Sweden.: pp 144-145.
    Google Scholar
  • 2 Surakka V, Illi M, Isokoski P. Gazing and Frowning As a New Technique for Human-Computer Interaction. ACM Transactions on Applied Perception 2004; 1: 40-56.
    CrossrefPubMedGoogle Scholar
  • 3 Su Y, Wolczowski A, Fisher MH, Bell GD, Burn D, Gao R. Towards an EMG Controlled Prosthetic Hand Using a 3D Electromagnetic Positioning System. In: Instrumentation and Measurement Technology Conference; 2005 May 16-19. 1: 261-266.
    Google Scholar
  • 4 Barretto A, Scargle S, Adjouadi M. A practical EMG-based human-computer interface for users with motor disabilities. Journal of Rehabilitation Research and Development 2000; 37: 53-64.
    PubMedGoogle Scholar
  • 5 Paloheimo M. Quantitative surface electromyography (qEMG) applications in anesthesiology and critical care. Acta Anesthesiol Scand Supplementum 1993; 34: 83 pages.
    PubMedGoogle Scholar
  • 6 Richard C, Watt J, Polson S, Kanemoto A. Detecting Intra-Operative Awareness using Facial Electromyographic Monitoring. In: 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Amsterdam: 1996
    Google Scholar
  • 7 Stalberg E, Karlsson L. Simulation of the normal concentric needle electromyogram by using a muscle model. Clinical Neurophysiology 2001; 112: 464-471.
    CrossrefPubMedGoogle Scholar
  • 8 Merletti R, Lo Conte L, Avignone E, Cuglielminotti P. Modeling of surface myoelectric signals – Part l: Model implementation. IEEE Transactions on Biomedical Engineering. 1999; 46: 810-820.
    CrossrefPubMedGoogle Scholar
  • 9 U.S.. National Library of Medicine. 1995 – Visible Woman project. Available from: http://www.nlm. nih.gov/
    PubMedGoogle Scholar
  • 10 Bo WJ, Wolfman NT, Krueger WA, Carr JJ, Bowden RL, Meschan I. Basic Atlas of Sectional Anatomy with Correlated Imaging, 3rd ed. Philadelphia: W. B. Saunders Company; 1998
    Google Scholar
  • 11 Latikka J, Kuurne T, Eskola H. Conductivity of living intracranial tissues. Physics in Medicine and Biology 2001; 46: 1611-1666.
    CrossrefPubMedGoogle Scholar
  • 12 Duck F. Physical Properties of Tissue: A Comprehensive Reference Book. San Diego: Academic Press; 1990
    Google Scholar
  • 13 Gabriel C, Gabriel S. Compilation of the dielectric properties of body tissues at RF and microwave frequencies. Brooks Air Force Technical Report AL/OE-TR-1996-0037.
    PubMedGoogle Scholar
  • 14 Oostendorp T, Delbeke J, Stegeman D. The Conductivity of the Human Skull: Results of In Vivo and In Vitro Measurements. IEEE Transactions on Biomedical Engineering 2000; 47: 1487-1492.
    CrossrefPubMedGoogle Scholar
  • 15 Malmivuo J, Plonsey R. Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields. New York: Oxford University Press; 1995
    Google Scholar
  • 16 Vehkaoja A, Verho J, Puurtinen M, Nöjd N, Lekkala J, Hyttinen J. Wireless head cap for EOG and facial EMG measurements. In: IEEE-EMBS 27th Annual International Conference; 2005 Jan 17-18; (6): 5865-5868
    Google Scholar