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Warianty tytułu
Języki publikacji
Abstrakty
In the last decade of the XX-th century, several academic centers have launched intensive research programs on the brain-computer interface (BCI). The current state of research allows to use certain properties of electromagnetic waves (brain activity) produced by brain neurons, measured using electroencephalographic techniques (EEG recording involves reading from electrodes attached to the scalp - the non-invasive method - or with electrodes implanted directly into the cerebral cortex - the invasive method). A BCI system reads the user's "intentions" by decoding certain features of the EEG signal. Those features are then classified and "translated" (on-line) into commands used to control a computer, prosthesis, wheelchair or other device. In this article, the authors try to show that the BCI is a typical example of a measurement and control unit.
Czasopismo
Rocznik
Tom
Strony
427--444
Opis fizyczny
Bibliogr. 41 poz., rys., tab.
Twórcy
autor
autor
autor
- Warsaw University of Technology, Institute of the Theory of Electrical Engineering, Measurement and Information Systems, Koszykowa 75, 00-662 Warsaw, Poland, remigiusz.rak@ee.pw.edu.pl
Bibliografia
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- [2] Wolpaw, J.R., Birbaumer, N. , McFarland, D. J., Pfurtscheller, G., Vaughan, T.M. (2002) Brain-computer interfaces for communication and control. Clin. Neurophysiol, 113, 767-791
- [3] Donoghue, J.P. (2002). Connecting cortex to machines: recent advances in brain interfaces. Nat. Neurosci, 5, 1085-1088.
- [4] Kołodziej, M., Majkowski, A., Rak, R. (2012). Linear discriminant analysis as a feature reduction technique of EEG signal for brain-computer interfaces. Electrotechnical Review, 88, 28-30.
- [5] Schwartz, A.B. (2004). Cortical neural prosthetics. Annu. Rev. Neurosci, 27, 487-507.
- [6] Farwell, L.A., Donchin, E. (1988). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalographz Clin. Neurophysiol, 70, 510-532.
- [7] Graimann, B., Allison, B., Pfurtscheller, G. (2011). Brain-Computer Interfaces, ISBN: 978-3-642-02090-2.
- [8] Vidal, J.J. (1977). Real-time detection of brain events in EEG. In IEEE Proc: Special Issue on BiolSignal Processing and Analysis, 65, 633-664.
- [9] Dornhege, G., Blankertz, B., Curio, G., Mulle, K. (2004). Boosting bit rates in non-invasive EEG single-trial classifications by feature combination and multi-class paradigms. IEEE Transactions on Biomedical Engineering , 51, 993-1002.
- [10] Wolpaw, J.R., McFarland, D.J., Vaughan, T.M. (2000). Brain-computer interface research at the Wadsworth Center. IEEE Transactions on Neural Systems and Rehabilitation Engineering , 8, 222-226.
- [11] Bauernfeind, G., Leeb, R., Wriessnegger, S.C., Pfurtscheller, G. (2008). Development, set-up and first results for a one-channel near-infrared spectroscopy system. Biomedizinische Technik, 53, 36-46.
- [12] Burghoff, M., Albrecht, H.-H., Hartwig, S., Hilschenz, I., Körber, R., Sander-Thömmes, T., Scheer, H., Voigt, J., Trahms, L. (2009). Squid system for meg and low field magnetic resonance. Metrol. Meas. Syst., 16(3), 371-376.
- [13] Sanei, S., Chambers, J.A. (2007). EEG Signal Processing, ISBN: 978-0-470-02581-9.
- [14] Celesia, G.G., Peachey, N.S. (2004). Visual Evoked Potentials and Electroretinograms. In E. Niedermeyer and F.H.L da Silva (Eds.), Electroencephalography: Basic principles, clinical applications, and related fields, Williams and Wilkins, Baltimore, MA.
- [15] Kołodziej, M., Majkowski, A., Rak, R. (2011). A new method of EEG classification for BCI with feature extraction based on higher order statistics of wavelet components and selection with genetic algorithms. Adaptive and Natural Computing Algorithms Lecture Notes in Computer Science, 6593, 280-289.
- [16] Donchin, E., Ritter, W., McCallum, C. (1978). Cognitive psychophysiology: the endogenous components of the ERP. Brain-event related potentials in man, Academic, New York.
- [17] Regan, D. (1977). Steady-state evoked potentials. J. Opt. Soc. Am., 67,1475-1489.
- [18] Allison, B.Z., McFarland, D.J., Schalk, G., Zheng, S.D., Jackson, M.M., Wolpaw, J.R. (2008). Towards an independent brain-computer interface using steady state visual evoked potentials. Clin. Neurophysiol, 119, 339-408
- [19] Byczuk, M. (2011). Rotary visual stimulator for research into visual evoked potentials. Electrotechnical Review, 87, 49-51.
- [20] Byczuk, M., Poryzała, P., Materka, A. (2011). On possibility of stimulus parameter selection for SSVEP-based brain-computer interface. Advances in Intelligent and Soft Computing, 103, 57-64.
- [21] Donchin, E., Spencer, K.M., Wijesinghe, R. (2000). The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans Rehabil. Eng., 8, 174-179.
- [22] Schalk, G., McFarland, D., Hinterberger, T., Birbaumer, N., Wolpaw, J. (2004). BCI2000: A General-Purpose Brain-Computer Interface (BCI) System. IEEE Transactions on Biomedical Engineering, 51, 10340-10430.
- [23] Byczuk, M., Materka, A. (2003). EEG signal-based human-computer communication. Department of Electrotechnics of Łódź University of Technology. (in Polish)
- [24] Nijholt, A., Tan, D. (2008). Brain-Computer Interfacing for Intelligent Systems. Intell. Syst. IEEE, 23, 72-79.
- [25] Hyekyoung, Lee, Cichocki, A., Seungjin, Choi. (2009). Kernel nonnegative matrix factorization for spectral EEG feature extraction. Neurocomputing,72, 3182-3190.
- [26] Pfurtscheller, G. (1999). EEG event-related desynchronization (ERD) and event-related synchronization (ERS). In: E. Niedermeyer and L.F.H. da Silva (Eds.), Electroencephalography: Basic principles, clinical applications and related fields. Williams and Wilkins, Baltimore, MD.
- [27] Rak, R., Kołodziej, M. (2008). Implementation of EEG signal spectrum in Brain-Computer Interface design. Electrotechnical Review, 84, 283-286. (in Polish)
- [28] Ince, N., Fikri, Goksu, Tewfik, A., Sami, Arica. (2009). Adapting subject specific motor imagery EEG patterns in space-time-frequency for a brain computer interface. Biomedical Signal Processing and Control, 4, 236-246.
- [29] McFarland, D.J., Miner, L.A., Vaughan, T.M., Wolpaw, J.R. (2000). Mu and Beta rhythm topographies during motor imagery and actual movements. Brain Topogr., 12, 117-86.
- [30] Elbert, T., Rockstroh, B., Lutzenberger, W., Birbaumer, N. (1980). Biofeedback of slow cortical potentials. I. Electroencephalogr Clin. Neurophysiol, 48, 293-301.
- [31] Bang-hua, Yanga, Guo-zheng, Yanb, Ting, Wub, Rong-guo, Yanb. (2007). Subject-based feature extraction using fuzzy wavelet packet in brain-computer interfaces. Signal Processing, 87, 1569-1574.
- [32] Kołodziej, M., Majkowski, A., Rak, R. (2009). Matlab FE_Toolbox - an universal utility for feature extraction of EEG signals for Brain-Computer Interface realization. Electrotechnical Review, 86, 44-46.
- [33] Kołodziej, M., Majkowski, A., Rak, R. (2010). A new method of feature extraction from EEG signal for brain-computer interface design. Electrotechnical Review, 86, 35-38.
- [34] Incea, N., Ahmed, H. Tewfika, Sami, Aricab. (2007). Extraction subject-specific motor imagery time-frequency patterns for single trial EEG classification. Computers in Biology and Medicine, 37, 499-508.
- [35] Majkowski, A., Kołodziej, M., Rak, R. (2012). Implementation of automatic feature selection methods for BCI realization IEEE International Instrumentation and Measurement Technology Conference - I2MTC.
- [36] Gutiérrez, D., Escalona-Vargas, D. (2010). EEG data classification through signal spatial redistribution and optimized linear discriminants. Computer methods and programs in biomedicine, 97, 39-47.
- [37] Kołodziej, M., Majkowski, A., Rak, R. (2010). Implementation of genetic algorithms to feature selection for the use of brain-computer interface. Electrotechnical Review, 87, 71-73.
- [38] Majkowski, A., Kołodziej, M., Rak, R. (2012). Implementation of selected EEG signal processing algorithms in asynchronous BCI. In Proc. IEEE International Symposium on Medical Measurements and Applications - MeMeA’2012.
- [39] Obermaier, B., Neuper, C., Guger, C., Pfurtscheller, G. (2001). Information Transfer Rate in a Five-Classes Brain-Computer Interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 9, 283-288.
- [40] Wolpaw, J.R., Loeb, G.E., Allison, B.Z., Donchin, E., Nascimento, O.F., Heetderks, W.J., Nijboer, F., Shain, W.G., Turner, J.N. (2006). BCI Meeting 2005 - workshop on signals and recording methods. IEEE Trans. Neural Syst. Rehabil. Eng.: A Pub IEEE Eng. Med. Biol. Soc., 14, 138-141.
- [41] Garcia, M.G.N. (2004). Direct brain-computer communication through scalp recorded EEG signals. Ëcole Polytechnique F D Rale De Lausanne.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW1-0105-0001