Abstract
The development of brain interface (BI) technology continues to attract researchers with a wide range of backgrounds and expertise. Though the BI community is committed to accurate and objective evaluation of methods, systems, and technology, the very diversity of the methods and terminology used in the field hinders understanding and impairs technology cross-fertilization and cross-group validation of findings. Underlying this dilemma is a lack of common perspective and language. As seen in our previous works in this area, our approach to remedy this problem is to propose language in the form of taxonomy and functional models. Our intent is to document and validate our best thinking in this area and publish a perspective that will stimulate discussion. We encourage others to do the same with the belief that focused discussion on language issues will accelerate the inherently slow natural evolution of language selection and thus alleviate related problems. In this work, we propose a theoretical framework for describing BI-technology-related studies. The proposed framework is based on the theoretical concepts and terminology from classical science, assistive technology development, human–computer interaction, and previous BI-related works. Using a representative set of studies from the literature, the proposed BI study framework was shown to be complete and appropriate perspective for thoroughly characterizing a BI study. We have also demonstrated that this BI study framework is useful for (1) objectively reviewing existing BI study designs and results, (2) comparing designs and results of multiple BI studies, (3) designing new studies or objectively reporting BI study results, and (4) facilitating intra- and inter-group communication and the education of new researchers. As such, it forms a sound and appropriate basis for community discussion.
Similar content being viewed by others
References
Allison, B. Z., and J. A. Pineda. ERPs evoked by different matrix sizes: Implications for a brain computer interface (BCI) system. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2):110–113, 2003.
Bailey, R. W. Human Performance Engineering. Englewood Cliffs, NJ: Prentice-Hall, 1989.
Baker, B. Using images to generate speech. Proceedings of the IEEE Biomedical Conference, Fort Worth, Texas, 1986
Bayliss, J. D. Use of the evoked potential P3 component for control in a virtual apartment. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2):113–116, 2003.
Birch, G. E., Z. Bozorgzadeh, and S. G. Mason. Initial on-line evaluation of the LF-ASD brain–computer interface with able-bodied and spinal-cord subjects using imagined voluntary motor potentials. IEEE Trans. Neural Syst. Rehabil. Eng. 11(1), 2002.
Blankertz, B., G. Dornhege, C. Schäfer, R. Krepki, J. Kolmorgen, K. R. Müller, V. Kunzmann, F. Losch, and G. Curio. Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis, IEEE 2003.
Cincotti, F., D. Mattia, C. Babiloni, F. Carducci, S. Salinari, L. Bianchi, M. G. Marciani, and F. Babiloni. The use of EEG modifications due to motor imagery for brain–computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2):131–133, 2003.
Cook, A. M., and S. M. Hussey. Assistive Technologies—Principles and Practice, 2nd ed. St. Louis: Mosby, 2002.
Creswell, J. W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, Sage Publications Inc., London, 2003.
Curran, E., P. Sykacek, M. Stokes, S. J. Roberts, W. Penny, I. Johnsrude, and A. M. Owen. Cognitive taskes for driving a brain–computer interface system: A pilot study. IEEE Trans. Neural Syst. Rehabil. Eng. 12(1):48–54, 2004.
Delorme, A., and S. Makeig. EEG changes accompanying learned regulation of 12-Hz EEG activity. IEEE Trans. Rehabil. Eng. (in press).
Dix, A. J., J. E. Finlay, G. D. Abowd, and R. Beale. Human–Computer Interaction. London: Prentic Hall Europe, 1998.
Donchin, E., K. M. Spencer, and R. Wijesinghe. The mental prosthesis: Assessing the speed of a P300-based brain–computer interface. IEEE Trans. Rehabil. Eng. 8(2):174–179, 2000.
Enabling America—Assessing the Role of Rehabilitation Science and Engineering. Washington, DC: National Academy Press, 1997.
Flather, M., H. Aston, and R. Stables. Handbook of Clinical Trials. London, UK: ReMedica Publishing, 2001.
Foley, J. D., A. van Dam, S. K. Feiner, and J. F. Hughes. Computer Graphics Principles and Practice, 2nd ed. New York: Addison-Wesley Publishing Inc., 1990.
Gao, X., D. Xu, M. Cheng, and S. Gao. A BCI-based environmental controller for the motion-disabled. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2):137–140, 2003.
Garrett, D., D. A. Peterson, C. W. Anderson, and M. H. Thaut. Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2):141–144, 2003.
Hix, D., and H. R. Hartson. Developing User Interfaces—Ensuring Usability Through Produce & Process. New York: John Wiley and Sons, 1993.
Kennedy, P. R., R. A. Bakay, M. M. Moore, K. Adams, and J. Goldwaithe. Direct control of a computer from the human central nervous system. IEEE Trans. Rehabil. Eng. 8(2):198–202, 2000.
King, T. W. Assistive Technology—Essential Human Factors. Needham Heights: Allyn & Bacon, 1999.
Kipke, D. R., R. J. Vetter, J. C. Williams, and J. F. Hetke. Silicon-substrate intracortical microelectrode arrays for long-term recording of neuronal spike activity in cerebral cortex. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2):151–155, 2003.
Kronegg, J., S. Voloshynovskiy, and T. Pun. Brin-Computer INterface model: Upper-capacity bound, signal-to-noise ratio estimation, and optimal number of symbols. Technical Report # 04.03—Computer Vision Group, Computing Science Center, University of Geneva, 2004.
Kubler, A., K. Schmidt, L. G. Cohen, M. Lotze, S. Winter, T. Hinterberger, and N. Birbaumer. Modulation of slow cortical potentials by transcranial magnetic stimulation in humans. Neurosci. Lett. 324(3):205–208, 2002.
Levine, S. P., J. E. Huggins, S. L. Bement, R. K. Kushwaha, L. A. Schuh, M. M. Rohde, E. A. Passaro, D. A. Ross, K. V. Elisevich, and B. J. Smith. A direct brain interface based on event-related potentials. IEEE Trans. Rehabil. Eng. 8(2):180–185, 2000.
Mason, S. G., A. Bashashati, M. Fatourechi, and G. E. Birch. Conceptual models for brain-interface technology design. http://www.braininterface.org/publishedlinks/BIDesignFramework.htm, 2005.
Mason, S. G., and G. E. Birch. Temporal control paradigms for direct brain interfaces—Rethinking the definition of asynchronous and synchronous. To be published in Proceeding of HCI International, Las Vegas, USA, 2005.
Mason, S. G., and G. E. Birch. A brain-controlled switch for asynchronous control applications. IEEE Trans. Biomed. Eng. 47(10):1297–1307, 2000.
Mason, S. G., and G. E. Birch. A general framework for brain–computer interface design. IEEE Trans. Neural Syst. Rehabil. Eng. 11(1):70–85, 2003.
Mason, S. G., M. M. Moore, and G. E. Birch. Designing pointing devices using brain–computer interface technology. Proceedings of IEEE 1st International Conference on Neural Engineering, Capri, Italy, 2003.
McFarland, D. J., W. A. Sarnacki, and J. R. Wolpaw. Brain–computer interface (BCI) operation: Optimizing information transfer rates. Biol. Psychol. 63(3):237–251, 2003.
Millan, J. R., J. Mourino, M. Franze, F. Cincotti, M. Varsta, J. Heikkonen, and F. Babilioni. A local neural classifier for the recognition of EEG patterns associated to mental tasks. IEEE Trans. Neural Netw. 13(3):678–686, 2003.
Montgomery, D. C. Design and Analysis of Experiments, 5th ed., John Wiley and Sons, N.Y., 2003.
Moore, M. M. Real world applications for brain–computer interface technology. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2):162–165, 2003.
Moore, M. M., S. G. Mason, and G. E. Birch. Analyzing trends in brain interface technology: A method to compare studies accepted for publication (July 2005) in IEEE Trans. Biomed. Eng.
Neumann, N., T. Hinterberger, J. Kaiser, U. Leins, N. Birbaumer, and A. Kubler. Automatic processing of self-regulation of slow cortical potentials: Evidence from brain–computer communication in paralysed patients. Clin. Neurophysiol. 115(3):628–635, 2004.
Neumann, N., A. Kubler, J. Kaiser, T. Hinterberger, and N. Birbaumer. Conscious perception of brain states: Mental strategies for brain–computer communication. Neuropsychologia 41(8):1028–1036, 2003.
Nicolelis, M. A. L. Brain–machine interfaces to restore motor function and probe neural circuits. Nat. Rev. Neurosci. 4(5):417–422, 2003.
Nielsen, J. Usability Engineering. Orlando, Florida: AP Professional, 2003.
Norman, K. L. The Psychology of Menu Selection: Designing Cognitive Control at the Human/Computer Interface. Norwood, NJ: Ablex Publishing Corp., 1991.
Obermaier, B., G. R. Muller, and G. Pfurtscheller. Virtual keyboard controlled by spontaneous EEG activity. IEEE Trans. Neural Syst. Rehabil. Eng. 11(4):422–426, 2003.
Omrod, J. E. Human Learning, 2nd ed. Englewood Cliffs, NJ: Merrill Prentice Hall, 1995.
Piantadosi, S. Clinical Trials: A Methodologic Perspective. New York: Wiley-Interscience, 1997.
Scherer, R., G. R. Muller, C. Neuper, B. Graimann, and G. Pfurtscheller. An asynchronously controlled EEG-based virtual keyboard: Improvement of the spelling rate. IEEE Trans. Biomed. Eng. 51(6):979–984, 2004.
Schlogl, A., C. Keinrath, R. Scherer, and G. Pfurtscheller. Information transfer of an EEG-based brain–computer interface. IEEE, 2003.
Serruya, M., N. Hatsopoulos, M. Fellows, L. Paninski, and J. P. Donoghue. Robustness of neuroprosthetic decoding algorithms. Biol. Cybern. 88(3):219–228, 2003.
Shadish, W. R., T. D. Cook, and D. T. Campbell. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston, MA: Houghton Mifflin Company, 2002.
Shneiderman, B. Designing the User Interface—Strategies for Effective Human–Computer Interaction, 3rd ed. New York: Addison-Wesley Longman Inc., 1998.
Small Clinical Trials—Issues and Challenges. Washington, DC: National Academy Press, 2001.
Taylor, D. M., S. I. H. Tillery, and A. B. Schwartz. Information conveyed through brain-control: Cursor versus robot. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2):195–199, 2003.
Trejo, L. J., K. R. Wheeler, C. C. Jorgensen, R. Rosipal, S. T. Clanton, B. Matthews, A. D. Hibbs, R. Matthews, and M. Krupka. Multimodal neuroelectric interface development. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2):199–204, 2003.
Trochim, W. The Research Methods Knowledge Base. Cincinnati, OH: Atomic Dog Publishing, 2001.
Vaughan, T., W. J. Heetderks, L. J. Trejo, W. Z. Rymer, M. Wienrich, M. M. Moore, A. Kubler, B. H. Dobkin, N. Birbaumer, E. Donchin, E. W. Wolpaw, and J. R. Wolpaw. Brain–computer interface technology: A review of the second international meeting. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2):94–109, 2003.
Wessberg, J., C. R. Stambaugh, J. D. Kralik, P. D. Beck, M. Laubach, J. K. Chapin, J. Kim, S. J. Biggs, M. A. Srinivasan, and M. A. Nicolelis. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408(6810):361–365, 2000.
Whitley, B. E. Principles of Research in Behavioral Science. New York: McGraw-Hill Humanities, 1996.
Wolpaw, J. R., N. Birbaumer, W. J. Heetderks, D. J. McFarland, P. H. Peckham, G. Schalk, E. Donchin, L. A. Quatrano, C. J. Robinson, and T. M. Vaughan. Brain–computer interface technology: A review of the first international meeting. IEEE Trans. Rehabil. Eng. 8(2):164–173, 2000.
Wolpaw, J. R., N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan. Brain–computer interfaces for communication and control. Clin. Neurophysiol. 113(6):767–791, 2002.
Wolpaw, J. R., D. McFarland, and G. Pfurtscheller. EEG-based communication: Improved accuracy by reponse verification. IEEE Trans. Rehabil. Eng. 6(3):326–333, 1998.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mason, S.G., Jackson, M.M.M. & Birch, G.E. A General Framework for Characterizing Studies of Brain Interface Technology. Ann Biomed Eng 33, 1653–1670 (2005). https://doi.org/10.1007/s10439-005-7706-3
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/s10439-005-7706-3