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Przegląd metod sterowania maszynami przy użyciu maszyn myślowych

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Warianty tytułu
EN
Invasive and non-invasive methods of brain-computer interfaces
Języki publikacji
PL
Abstrakty
PL
W artykule przedstawiono charakterystykę inwazyjnych i nieinwazyjnych interfejsów mózg-maszyna oraz ich zastosowanie. Na obecnym etapie badań występuje szereg trudności technicznych, które ograniczają rozwój gotowych aplikacji. Przewiduje się wiele zastosowań tego typu interfejsów, począwszy od pomocy osobom chorym i niepełnosprawnym, użytkowników aplikacji komputerowych aż po sterowanie maszynami, procesami przemysłowymi czy urządzeniami gospodarstwa domowego.
EN
In the paper the characteristics of invasive and non-invasive methods of brain-computer interfaces and their adaptations are presented. This field of science has greatly evolved recently. At the current level of researches there are a number of technical difficulties that limit further development of existing applications. It is foreseen to use those applications to help physically handicapped patients or just to control various kinds of machines.
Rocznik
Strony
134--136
Opis fizyczny
Bibliogr. 28 poz., rys.
Twórcy
autor
autor
autor
autor
  • Politechnika Opolska
Bibliografia
  • [1] Moore M.M, Mason S.G., Birch G. E., Analyzing Trends in Brain Interface Technology: A Method to Compare Studies: Annals of Biomedical Engineering, Vol. 34, No. 5, 2006, 859-878
  • [2] Mason S.G., Moore M.M., Birch G.E., A General Framework for Characterizing Studies of Brain Interface Technology, Annals of Biomedical Engineering, Vol. 33, No. 11, 2005, 1653-1670
  • [3] Andersen R.A., Musallam S., Pesaran B., Selecting the signals for a brain–machine interface, Current Opinion in Neurobiology, Vol. 14, 2004, 720-726
  • [4] Greenfield S.A., Biotechnology, the brain and the future, Trends in Biotechnology, Vol. 23 No.1 , 2005, 34-41
  • [5] Georgopoulos A.P., Langheim F.J., Leuthold .C., Merkle A.N., Magneto-encephalographic signals predict movement trajectory in space, Exp Brain Res, Vol. 25, 2005, 132-135
  • [6] Millán J.R., Renkens F., Mourino J., Gerstner W., Brain-actuated interaction, Artificial Intelligence Vol. 159, 2004, 241-259
  • [7] Cincotti F., Babiloni F., Mattiocco M., Astol L.S., Marciani M.G., Mattia D., Laboratory of functional neuroelectrical imaging and brain–computer interfacing at Fondazione Santa Lucia, Cogn Process, Vol. 6, 2005, 75–83
  • [8] Kleber B., Birbaumer N., Direct brain communication: neuroelectric and metabolic approaches at Tübingen, Cogn Process, Vol. 6, 2005, s. 65-74
  • [9] Koizumi H., Maki A., Yamamoto T., Sato H., Yamamoto Y., Kawaguchi H., Non-invasive brain-function imaging by optical topography, Trends in Analytical Chemistry, Vol. 24, No. 2, 2005, 147-157
  • [10] Krausz G., Scherer R., Korisek G., Pfurtscheller G., Critical Decision-Speed and Information Transfer in the “Graz Brain–Computer Interface”, Applied Psychophysiology and Biofeedback, Vol. 28, No. 3, 2003, 233-240
  • [11] Schwartz A.B., Taylor D.M., Tillery S.I.H., Extraction algorithms for cortical control of arm prosthetics. Curr Opin Neurobiol, Vol. 11, 2001, 701-708
  • [12] Schaal S., Schweighofer N., Computational motor control in humans and robots, Current Opinion in Neurobiology, Vol. 15, 2005, 675-682
  • [13] Lal T.N., Hinterberger T., Widman G., Schroeder M., Hill J., Rosentiel W., Elger C.E., Schoelkopf B., Birbaumer N., Methods Towards Invasive Human Brain Computer Interfaces, Workshop on Verification, Validation and Testing of Learning Systems, NIPS-2004, British Columbia, 2004, 1-8
  • [14] Martin R., Mind Control, Wired Magazine, Vol. 13, No. 3, 2005, 1-5
  • [15] Matsumotoa G., Tsujinob H., Design of a brain computer using the novel principles of output-driven operation and memory-based architecture, International Congress Series 1250, 2003, 529-546
  • [16] Arbib M.A., Fellous J.M., Emotions: from brain to robot, Trends in Cognitive Sciences Vol. 8 No. 12, 2004, 554-561
  • [17] Hung C., Lee P., Wu Y., Chen L. Yeh T., Hsieh J., Recognition of Motor Imagery Electroencephalography Using Independent Component Analysis and Machine Classifiers, Annals of Biomedical Engineering, Vol. 33, No. 8, 2005, 1053–1070
  • [18] Shenoy P., Rao R.P.N., Dynamic Bayesian Networks for Brain-Computer Interfaces, Workshop on Verification, Validation and Testing of Learning Systems, NIPS-2004, British Columbia, 2004, s. 670-677
  • [19] Neuper C., Mueller G.R., Kuebler A., Birbaumer N., Pfurtscheller G., Clinical application of an EEG-based brain–computer interface: a case study in a patient with severe motor impairment, Clinical Neurophysiology, Vol. 114, 2003, 399-409
  • [20] Cremades J.G., Barreto A., Sanchez F., Adjouadi D., Human–computer interfaces with regional lower and upper alpha frequencies as on-line indexes of mental activity, Computers in Human Behavior, Vol. 20, 2004, 569-579
  • [21] Zmarzły D., Pomiary elektrycznych wielkości medycznych, Oficyna Wydawnicza Politechniki Opolskiej, Nr 268, 2005
  • [22] Begg R., Kamruzzaman J., A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data, Journal of Biomechanics, Vol. 38, 2005, 401-408
  • [23] Ferdinando A. Mussa-Ivaldi F.A., Miller L.E., Brain–machine interfaces: computational demands and clinical needs meet basic neuroscience, Trends in Neurosciences Vol.26 No.6, 2003, 329-335
  • [24] Newell K.M. Liu Y.T., Mayer-Kress G., Learning in the brain–computer interface: insights about degrees of freedom and degeneracy from a landscape model f motor learning, Cogn Process, Vol. 6, 2005, 37-47
  • [25] Perelman Y., Ginosar R., Analog frontend for multichannel neuronal recording system with spike and LFP separation, Journal of Neuroscience Methods, Vol. 153, 2006, 21-26
  • [26] Zeigler B.P., The brain-machine disanalogy revisited, BioSystems, Vol. 64, 2002, 127-140
  • [27] Hollenberg B.A., Richards C.D., Richards R., Bahr D.F., Rector D.M., A MEMS fabricated flexible electrode array forrecording surface field potentials, Journal of Neuroscience Methods, Vol. 153, 2006, 147-153
  • [28] Yamasaki H., The future of sensor electronics, Sensor and Actuators Nr. A56, 1996, 129-133
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPOK-0021-0005
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