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Describing Different Brain Computer Interface Systems Through a Unique Model: A UML Implementation

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Abstract

All the protocols currently implemented in brain computer interface (BCI) experiments are characterized by different structural and temporal entities. Moreover, due to the lack of a unique descriptive model for BCI systems, there is not a standard way to define the structure and the timing of a BCI experimental session among different research groups and there is also great discordance on the meaning of the most common terms dealing with BCI, such as trial, run and session. The aim of this paper is to provide a unified modeling language (UML) implementation of BCI systems through a unique dynamic model which is able to describe the main protocols defined in the literature (P300, μ-rhythms, SCP, SSVEP, fMRI) and demonstrates to be reasonable and adjustable according to different requirements. This model includes a set of definitions of the typical entities encountered in a BCI, diagrams which explain the structural correlations among them and a detailed description of the timing of a trial. This last represents an innovation with respect to the models already proposed in the literature. The UML documentation and the possibility of adapting this model to the different BCI systems built to date, make it a basis for the implementation of new systems and a mean for the unification and dissemination of resources. The model with all the diagrams and definitions reported in the paper are the core of the body language framework, a free set of routines and tools for the implementation, optimization and delivery of cross-platform BCI systems.

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References

  • Allison, B. Z., & Pineda, J. A. (2003). ERPs evoked by different matrix sizes: Implications for a brain computer interface (BCI) System. IEEE Transactions on Neural Systems Rehabilitations Engineering, 11(2), 110–113.

    Article  Google Scholar 

  • Bayliss, J. D. (2001). A flexible brain computer interface. PhD dissertation, Univ. Rochester, Rochester, New York, 2001.

  • Bianchi, L., Babiloni, F., Cincotti, F., Salinari, S., & Marciani, M. G. (2003). Introducing BF++: A C++ framework for cognitive bio-feedback systems design. Methods of Information in Medicine, 42(1), 104–110.

    PubMed  CAS  Google Scholar 

  • Bianchi, L., Quitadamo, L. R., Garreffa, G., Cardarilli, G. C., & Marciani, M. G. (2007a). Performances evaluation and optimization of brain–computer interface systems in a copy spelling task. IEEE Transactions on Neural Systems Rehabilitations Engineering, 15(2), 207–216.

    Article  Google Scholar 

  • Bianchi, L., Quitadamo, L. R., Marciani, M. G., Maraviglia, B., Abbafati, M., & Garreffa, G. (2007b). How the NPX data format handles EEG data acquired simultaneously with fMRI. Magnetic Resonane Imaging, 25(6), 1011–1014.

    Article  Google Scholar 

  • Blankertz, B., Müller, K. R., Curio, G., Vaughan, T. M., Schalk, G., Wolpaw, J. R., Schlögl, A., Neuper, C., Pfurtscheller, G., Hinterberger, T., Schröder, M., & Birbaumer, N. (2004). The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE Transactions on Biomedical Engineering, 51(6), 1044–1051.

    Article  PubMed  Google Scholar 

  • Borisoff, J. F., Mason, S. G., & Birch, G. E. (2006). Brain interface research for asynchronous control application. IEEE Transactions on Neural Systems Rehabilitations Engineering, 14(2), 160–164.

    Article  Google Scholar 

  • Donchin, E., Spencer, K. M., & Wijesinghe, R. (2000). The mental prosthesis: Assessing the speed of a P300-based brain–computer interface. IEEE Transactions on Rehabilitations Engineering, 8(2), 174–179.

    Article  CAS  Google Scholar 

  • Fabiani, G. E., McFarland, D. J., Wolpaw, J. R., & Pfurtsheller, G. (2004). Conversion of EEG activity into cursor movement by a brain–computer interface (BCI). IEEE Transactions on Neural Systems Rehabilitations Engineering, 12(3), 331–338.

    Article  Google Scholar 

  • Fowler, M. (2003). UML distilled a brief guide to the Standard Object Modeling Language, 3rd ed.. Boston: Addison-Wesley.

    Google Scholar 

  • Guger, C., Schögl, A., Neuper, C., Walterspacher, D., Strein, T., & Pfurtscheller, G. (2001). Rapid prototyping of an EEG-based brain–computer interface (BCI). IEEE Transactions on Neural Systems Rehabilitations Engineering, 9(1), 49–58.

    Article  CAS  Google Scholar 

  • Hinterberger, T., Kübler, A., Kaiser, J., Neumann, N., & Birbaumer, N. (2003a). A brain–computer interface (BCI) for the locked-in: Comparison of different EEG classifications for the thought translation device. Clinical Neurophysiology, 114(3), 416–425.

    Article  PubMed  Google Scholar 

  • Hinterberger, T., Mellinger, J. & Birbaumer, N. (2003b). The Thought Translation Device: structure of a multimodal brain–computer communication system. Proceedings of the 1st international IEEE EMBS Conference on Neural Engineering, Capri Island, Italy, March 20–22, 2003, 603–606.

  • Krusienski, D. J., Sellers, E. W., Cabestaing, F., Bayoudh, S., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2006). A comparison of classification techniques for the P300 speller. Journal of Neural Engineering, 3(4), 299–305.

    Article  PubMed  Google Scholar 

  • Kübler, A., Neumann, N., Kaiser, J., Kotchoubey, B., Hinterberger, T., & Birbaumer, N. P. (2001). Brain–computer communication: Self-regulation of slow cortical potentials for verbal communication. Archives of Physical Medicine and Rehabilitation, 82(11), 1533–1539.

    Article  PubMed  Google Scholar 

  • Leuthardt, E. C., Schalk, G., Wolpaw, J. R., Ojemann, J. G., & Moran, D. W. (2004). A brain–computer interface using electrocorticographic signals in humans. Journal of Neural Engineering, 1(2), 63–71.

    Article  PubMed  Google Scholar 

  • Mason, S. G., & Birch, G. E. (2000). A brain-controlled switch for asynchronous control applications. IEEE Transactions on Biomedical Engineering, 47(10), 1297–1307.

    Article  PubMed  CAS  Google Scholar 

  • Mason, S. G., Moore Jackson, M. M., & Birch, G. E. (2005). A general framework for characterizing studies of brain interface technology. Annals of Biomedical Engineering, 33(11), 1653–1670.

    Article  PubMed  CAS  Google Scholar 

  • Neuper, C., Müller, G. R., Kübler, A., Birbaumer, N., & Pfurtscheller, G. (2003). Clinical application of an EEG-based brain–computer interface: A case study in a patient with severe motor impairment. Clinical Neurophysiology, 114(3), 399–409.

    Article  PubMed  CAS  Google Scholar 

  • Obermaier, B., Neuper, C., Guger, C., & Pfurtscheller, G. (2001). Information transfer rate in a five-classes brain–computer interface. IEEE Transactions on Neural Systems Rehabilitations Engineering, 9(3), 283–288.

    Article  CAS  Google Scholar 

  • OMG (2003). OMG unified modeling language specification. Available online at http://www.omg.org/docs/formal/03-03-04.pdf

  • Pfurtscheller, G., Neuper, C., Sclögl, A., & Lugger, K. (1998). Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Transactions on Biomedical Engineering, 6(3), 316–325.

    CAS  Google Scholar 

  • Pregenzer, M., & Pfurtscheller, G. (1999). Frequency component selection for an EEG-based brain to computer interface. IEEE Transactions on Neural System Rehabilitations Engineering, 7(4), 413–419.

    Article  CAS  Google Scholar 

  • Quitadamo, L. R., Abbafati, M., Saggio, G., Marciani, M. G., & Bianchi L. (2007a). Brain–computer interface research at the Neuroscience Department of the “Tor Vergata” University of Rome, Italy. Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, August 23–26, 2007, pp 4715–4718.

  • Quitadamo, L. R., Marciani, M. G., & Bianchi, L. (2007b). Optimization of brain–computer interface systems by means of XML and BF++Toys. International Journal of Bioelectromagnetism, 9(3), 172–184.

    Google Scholar 

  • Schalk, G., McFarland, D. J., Hinterberger, T., Birbaumer, N., & Wolpaw, J. R. (2004). BCI2000: a general-purpose brain–computer interface. IEEE Transactions on Biomedical Engineering, 51(9), 1034–1043.

    Article  PubMed  Google Scholar 

  • Scherer, R., Müller, G. R., Neuper, C., & Graimann, B. (2004). An asynchronously controlled EEG-based virtual keyboard: Improvement of the spelling rate. IEEE Transactions on Biomedical Engineering, 51(6), 979–984.

    Article  PubMed  Google Scholar 

  • Sellers, E. W., & Donchin, E. (2006a). A P300-based brain–computer interface: Initial tests by ALS patients. Clinical Neurophysiology, 117(3), 538–548.

    Article  PubMed  Google Scholar 

  • Sellers, E. W., Krusienski, D. J., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2006b). A P300 event-related potential brain–computer interface (BCI): The effects of matrix size and inter stimulus interval on performance. Biological Psychology, 73(3), 242–252.

    Article  PubMed  Google Scholar 

  • Sellers, E. W., Kübler, A., & Donchin, E. (2006c). Brain–Computer interface research at the University of South Florida cognitive psychophysiology laboratory: The P300 speller. IEEE Transactions on Neural Systems Rehabilitations Engineering, 14(2), 221–224.

    Article  Google Scholar 

  • Serby, H., Yom-Tov, E., & Inbar, G. F. (2005). An Improved P300-Based Brain Computer Interface. IEEE Transactions on Neural Systems Rehabilitations Engineering, 13(1), 88–98.

    Google Scholar 

  • Townsend, G., Grainmann, B., & Pfurtscheller, G. (2004). Continuous EEG classification during motor imagery—Simulation of an asynchronous BCI. IEEE Transactions on Neural Systems Rehabilitations Engineering, 12(2), 258–265.

    Article  Google Scholar 

  • Trejo, L. J., Rosipal, R., & Matthews, B. (2006). Brain–computer interfaces for 1-D and 2-D cursor control: Designs using volitional control of the EEG spectrum or steady-state visual evoked potentials. IEEE Transactions on Neural Systems Rehabilitations Engineering, 14(2), 225–229.

    Article  Google Scholar 

  • Wang, Y., Wang, R., Gao, X., Hong, B., & Gao, S. (2006). A practical VEP-based brain computer interface. IEEE Transactions on Neural Systems Rehabilitations Engineering, 14(2), 234–239.

    Article  CAS  Google Scholar 

  • Weiskopf, N., Mathiak, K., Bock, S. W., Scharnowski, F., Veit, R., Grodd, W., Goebel, R., & Birbaumer, N. (2004). Principles of a brain–computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI). IEEE Transactions on Biomedical Engineering, 51(6), 966–970.

    Article  PubMed  Google Scholar 

  • Wilson, J. A., Felton, E. A., Garell, P. C., Schalk, G., & Williams, J. C. (2006). ECoG factors underlying multimodal control of a brain–computer interface. IEEE Transactions on Neural Systems Rehabilitations Engineering, 14(2), 246–250.

    Article  Google Scholar 

  • Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain–computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767–791.

    Article  PubMed  Google Scholar 

  • Wolpaw, J. R., & McFarland, D. (2004). Control of a two-dimensional movement signal by a non invasive brain–computer interface in humans. Proceedings of the National Academy of Sciences USA, 101(51), 17849–17854.

    Article  CAS  Google Scholar 

  • Wolpaw, J. R., McFarland, D. J., Vaughan, T. M., & Schalk, G. (2003). The Wadsworth Center Brain–Computer Interface (BCI) Research and Development Program. IEEE Transactions on Neural Systems Rehabilitations Engineering, 11(2), 204–207.

    Article  Google Scholar 

  • Yoo, S. S., Fairneny, T., Chen, N. K., Choo, S. E., Panych, L. P., Park, H., Lee, S. Y., & Jolesz, F. A. (2004). Brain–computer interface using fMRI: Spatial navigation by thoughts. Neuroreport, 15(10), 1591–1595.

    Article  PubMed  Google Scholar 

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Acknowledgments

This work was partially supported by the DCMC (Disorders of Motor and Cardio-respiratory Control) Project of the Italian Space Agency and dedicated to the memory of Renato Grasso and Aleksandar Kostov.

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Correspondence to Luigi Bianchi.

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Quitadamo, L.R., Marciani, M.G., Cardarilli, G.C. et al. Describing Different Brain Computer Interface Systems Through a Unique Model: A UML Implementation. Neuroinform 6, 81–96 (2008). https://doi.org/10.1007/s12021-008-9015-0

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