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Journal of Medical Systems

, 40:34 | Cite as

A Modular Framework for EEG Web Based Binary Brain Computer Interfaces to Recover Communication Abilities in Impaired People

  • Giuseppe PlacidiEmail author
  • Andrea Petracca
  • Matteo Spezialetti
  • Daniela Iacoviello
Patient Facing Systems
Part of the following topical collections:
  1. Smart Living in Healthcare and Innovations

Abstract

A Brain Computer Interface (BCI) allows communication for impaired people unable to express their intention with common channels. Electroencephalography (EEG) represents an effective tool to allow the implementation of a BCI. The present paper describes a modular framework for the implementation of the graphic interface for binary BCIs based on the selection of symbols in a table. The proposed system is also designed to reduce the time required for writing text. This is made by including a motivational tool, necessary to improve the quality of the collected signals, and by containing a predictive module based on the frequency of occurrence of letters in a language, and of words in a dictionary. The proposed framework is described in a top-down approach through its modules: signal acquisition, analysis, classification, communication, visualization, and predictive engine. The framework, being modular, can be easily modified to personalize the graphic interface to the needs of the subject who has to use the BCI and it can be integrated with different classification strategies, communication paradigms, and dictionaries/languages. The implementation of a scenario and some experimental results on healthy subjects are also reported and discussed: the modules of the proposed scenario can be used as a starting point for further developments, and application on severely disabled people under the guide of specialized personnel.

Keywords

Binary BCI EEG Web applications Framework Prediction engine Motivational tools 

Notes

Acknowledgments

The Authors are very grateful to the “Fondazione Fabio Sciacca Onlus” for having supported this research project.

References

  1. 1.
    Wolpaw, J., Birbaumer, N., McFarland, D., Pfurtscheller, G., and Vaughan, T., Brain–computer interfaces for communication and control. Clin. Neurophysiol. 113:767–791, 2002. doi: 10.1016/s1388-2457(02)00057-3.PubMedCrossRefGoogle Scholar
  2. 2.
    Cincotti, F., Mattia, D., Aloise, F., Bufalari, S., Schalk, G., Oriolo, G., Cherubini, A., Marciani, M., and Babiloni, F., Non-invasive brain–computer interface system: Towards its application as assistive technology. Brain Res. Bull. 75:796–803, 2008. doi: 10.1016/j.brainresbull.2008.01.007.PubMedPubMedCentralCrossRefGoogle Scholar
  3. 3.
    Lebedev, M., and Nicolelis, M., Brain–machine interfaces: past, present and future. Trends Neurosci. 29:536–546, 2006. doi: 10.1016/j.tins.2006.07.004.PubMedCrossRefGoogle Scholar
  4. 4.
    Millán, J. (2013) Brain-Computer Interfaces. Introduction to Neural Engineering for Motor Rehabilitation 237–252. doi: 10.1002/9781118628522.ch12
  5. 5.
    Wolpaw, J., McFarland, D., Neat, G., and Forneris, C., An EEG-based brain-computer interface for cursor control. Electroencephalogr. Clin. Neurophysiol. 78:252–259, 1991. doi: 10.1016/0013-4694(91)90040-b.PubMedCrossRefGoogle Scholar
  6. 6.
    Williamson, J., Murray-Smith, R., Blankertz, B., Krauledat, M., and Müller, K., Designing for uncertain, asymmetric control: Interaction design for brain–computer interfaces. Int. J. Hum. Comput. Stud. 67:827–841, 2009. doi: 10.1016/j.ijhcs.2009.05.009.CrossRefGoogle Scholar
  7. 7.
    Pires, G., Torres, M., Casaleiro, N., Nunes, U. and Castelo-Branco, M. (2011) Playing Tetris with non-invasive BCI. 2011 I.E. 1st International Conference on Serious Games and Applications for Health (SeGAH). doi: 10.1109/segah.2011.6165454
  8. 8.
    D'albis, T., Blatt, R., Tedesco, R., Sbattella, L., and Matteucci, M., A predictive speller controlled by a brain-computer interface based on motor imagery. TOCHI. 19:1–25, 2012. doi: 10.1145/2362364.2362368.CrossRefGoogle Scholar
  9. 9.
    LaFleur, K., Cassady, K., Doud, A., Shades, K., Rogin, E., and He, B., Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface. J. Neural Eng. 10:046003, 2013. doi: 10.1088/1741-2560/10/4/046003.PubMedCrossRefGoogle Scholar
  10. 10.
    Yu, T., Li, Y., Long, J., and Li, F., A hybrid brain-computer interface-based mail client. Comput. Math. Methods Med. 2013:1–9, 2013. doi: 10.1155/2013/750934.Google Scholar
  11. 11.
    Niedermeyer, E., and Lopes da Silva, F., Electroencephalography: basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins, Philadelphia, 2005.Google Scholar
  12. 12.
    Subha, D., Joseph, P., Acharya, U.R., and Lim, C., EEG signal analysis: A survey. J. Med. Syst. 34:195–212, 2008. doi: 10.1007/s10916-008-9231-z.CrossRefGoogle Scholar
  13. 13.
    Bauer, G., Gerstenbrand, F., and Rumpl, E., Varieties of the locked-in syndrome. J. Neurol. 221:77–91, 1979. doi: 10.1007/bf00313105.PubMedCrossRefGoogle Scholar
  14. 14.
    Neuper, C., Müller-Putz, G., Scherer, R., and Pfurtscheller, G., Motor imagery and EEG-based control of spelling devices and neuroprostheses. Prog. Brain Res. 393–409, 2006. doi: 10.1016/s0079-6123(06)59025-9.
  15. 15.
    Furdea, A., Halder, S., Krusienski, D., Bross, D., Nijboer, F., Birbaumer, N., and Kübler, A., An auditory oddball (P300) spelling system for brain-computer interfaces. Psychophysiology. 46:617–625, 2009. doi: 10.1111/j.1469-8986.2008.00783.x.PubMedCrossRefGoogle Scholar
  16. 16.
    Lance, B., Kerick, S., Ries, A., Oie, K., and McDowell, K., Brain-computer interface technologies in the coming decades. Proc. IEEE. 100:1585–1599, 2012. doi: 10.1109/jproc.2012.2184830.CrossRefGoogle Scholar
  17. 17.
    Kaufmann, T., Holz, E., and Kübler, A., Comparison of tactile, auditory, and visual modality for brain-computer interface use: a case study with a patient in the locked-in state. Front. Neurosci., 2013. doi: 10.3389/fnins.2013.00129.PubMedPubMedCentralGoogle Scholar
  18. 18.
    Placidi, G., Avola, D., Petracca, A., Sgallari, F., and Spezialetti, M., Basis for the implementation of an EEG-based single-trial binary brain computer interface through the disgust produced by remembering unpleasant odors. Neurocomputing, 2015. doi: 10.1016/j.neucom.2015.02.034.Google Scholar
  19. 19.
    Blankertz, B., Muller, K., Curio, G., Vaughan, T., Schalk, G., Wolpaw, J., Schlogl, A., Neuper, C., Pfurtscheller, G., Hinterberger, T., Schroder, M., and Birbaumer, N., The BCI competition 2003: Progress and perspectives in detection and discrimination of EEG single trials. IEEE Trans. Biomed. Eng. 51:1044–1051, 2004. doi: 10.1109/tbme.2004.826692.PubMedCrossRefGoogle Scholar
  20. 20.
    Farwell, L., and Donchin, E., Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol. 70:510–523, 1988. doi: 10.1016/0013-4694(88)90149-6.PubMedCrossRefGoogle Scholar
  21. 21.
    Townsend, G., LaPallo, B., Boulay, C., Krusienski, D., Frye, G., Hauser, C., Schwartz, N., Vaughan, T., Wolpaw, J., and Sellers, E., A novel P300-based brain–computer interface stimulus presentation paradigm: Moving beyond rows and columns. Clin. Neurophysiol. 121:1109–1120, 2010. doi: 10.1016/j.clinph.2010.01.030.PubMedPubMedCentralCrossRefGoogle Scholar
  22. 22.
    Apache Project. The Apache HTTP Server Project. http://httpd.apache.org/. Accessed 3 Apr 2015
  23. 23.
    Tilkov, S., and Vinoski, S., Node.js: Using JavaScript to build high-performance network programs. IEEE Internet Comput. 14:80–83, 2010. doi: 10.1109/mic.2010.145.CrossRefGoogle Scholar
  24. 24.
    Node.js. http://nodejs.org/. Accessed 3 Apr 2015
  25. 25.
    MySQL: The world’s most popular open source database. http://www.mysql.com/. Accessed 3 Apr 2015
  26. 26.
    Zhao, S., Xia, X., and Le, J., A real-time web application solution based on node.js and WebSocket. Adv. Mater. Res. 816–817:1111–1115, 2013. doi: 10.4028/www.scientific.net/amr.816-817.1111.CrossRefGoogle Scholar
  27. 27.
    W3C HTML. http://www.w3.org/html. Accessed 3 Apr 2015
  28. 28.
  29. 29.
    CSS Cascading Style Sheets. http://www.w3.org/Style/CSS/. Accessed 3 Apr 2015
  30. 30.
    The Grid System. http://www.thegridsystem.org/. Accessed 3 Apr 2015
  31. 31.
    jQuery. In: Jquery.com. http://jquery.com. Accessed 3 Apr 2015
  32. 32.
    Socket RFC 147 - Definition of a socket. https://tools.ietf.org/html/rfc147. Accessed 3 Apr 2015
  33. 33.
    UDP RFC 768 - User Datagram Protocol. http://tools.ietf.org/html/rfc768. Accessed 3 Apr 2015
  34. 34.
    WebSocket.org - A WebSocket Community. http://www.websocket.org. Accessed 3 Apr 2015
  35. 35.
    The WebSocket API. http://www.w3.org/TR/2011/WD-websockets-20110419. Accessed 3 Apr 2015
  36. 36.
    Corpus.byu.edu. British National Corpus (BYU-BNC). http://corpus.byu.edu/bnc/. Accessed 3Apr 2015
  37. 37.
    Liber Liber. http://www.liberliber.it. Accessed 3 Apr 2015
  38. 38.
    Neuroelectrics ENOBIO. http://www.neuroelectrics.com/enobio. Accessed 3 Apr 2015
  39. 39.
    Jurcak, V., Tsuzuki, D., and Dan, I., 10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems. NeuroImage. 34:1600–1611, 2007. doi: 10.1016/j.neuroimage.2006.09.024.PubMedCrossRefGoogle Scholar
  40. 40.
    Placidi, G., Petracca, A., Spezialetti, M., and Iacoviello, D. (2015) Classification Strategies for a Single-Trial Binary Brain Computer Interface Based on Remembering Unpleasant Odors. Proceedings of 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 7019–7022.Google Scholar
  41. 41.
    Iacoviello, D., Petracca, A., Spezialetti, M., and Placidi, G., A real-time classification algorithm for EEG-based BCI driven by self-induced emotions. Comput. Methods Prog. Biomed., 2015. doi: 10.1016/j.cmpb.2015.08.011.Google Scholar
  42. 42.
    Draper, N., and Smith, H. (1998) Applied regression analysis. New York: Wiley 3rd Edition. isbn: 0-471-17082-8.Google Scholar
  43. 43.
    Pistoia, F., Carolei, A., Iacoviello, D., Petracca, A., Sacco, S., Sarà, M., Spezialetti, M., and Placidi, G., EEG-detected olfactory imagery to reveal covert consciousness in minimally conscious state. Brain Inj., 2015. doi: 10.3109/02699052.2015.1075251.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Giuseppe Placidi
    • 1
    Email author
  • Andrea Petracca
    • 1
  • Matteo Spezialetti
    • 1
  • Daniela Iacoviello
    • 2
  1. 1.Department of Life, Health and Environmental SciencesUniversity of L’AquilaL’AquilaItaly
  2. 2.Department of Computer, Control and Management Engineering Antonio RubertiSapienza University of RomeRomeItaly

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