Powiadomienia systemowe
- Sesja wygasła!
- Sesja wygasła!
Identyfikatory
Warianty tytułu
The impact of reducing the number of electrodes in the BCI system on evaluation of the brain electrical activity
Języki publikacji
Abstrakty
W artykule zaprezentowano wyniki analiz dotyczących aktywności elektrycznej mózgu ukierunkowanych na możliwość redukcji liczby elektrod w badaniu EEG wykonywanym na potrzeby asynchronicznego interfejsu mózg-komputer (BCI). Stosowne obliczenia potwierdzają zasadność wyboru zestawu 8 elektrod (tzn. F3, T7, C3, Cp1, C4, T8, F4 i Cz) w systemie BCI, wykorzystującym wyspecjalizowany wzmacniacz EEG firmy g.tec, skonstruowanym w IETiSIP Politechniki Warszawskiej.
The subject of the paper is evaluation of the brain electrical activity associated with imagining some specific motor actions for the needs of asynchronous Brain-Computer Interface (BCI) [1-4]. These analysis, called EEG inverse problems, can be useful among others to optimize the number and placement of electrodes. Dedicated calculations were carried out using the algorithm sLORETA (Section 3) [5-13]. The basis of the BCI interface is the ability to detect differences between the considered classes of tasks. In the case of asynchronous interfaces, the evaluation of brain activity in the frequency domain provides much more conclusive information than the time-domain analysis. These indicate that, although the best conditions for synchronous neuronal activity are in the range of delta waves (up to 4 Hz), the biggest differences between the compared classes are apparent in the alpha band (8-12 Hz) in the central parts of the cortex (Section 5; pic. 2,3). Moreover, the performed calculations show no significant difference in the location of the brain activity sources for the results obtained using the set of 32 electrodes and after the fourfold reduction in the number of electrodes. Thus, they confirm the relevance of the set of 8 electrodes (i.e. F3, T7, C3, CP1, C4, T8, F4, and Cz) in the BCI system constructed and used in the Department of Information and Measuring Systems of the Warsaw University of Technology (Sections 6,7; Fig. 5; Tab. 2).
Wydawca
Czasopismo
Rocznik
Tom
Strony
718--721
Opis fizyczny
Bibliogr. 15 poz., tab., rys., wykr.
Twórcy
autor
- Politechnika Warszawska, Instytut Elektrotechniki Teoretycznej i Systemów Informacyjno-Pomiarowych, ul. Koszykowa 75, 00-662 Warszawa
Bibliografia
- [1] Rak R.J., Kołodziej M., Majkowski A.: Brain-computer interface as measurement and control system – the review paper. Metrology and Measurement Systems, XIX’2012, no. 3, 427-444.
- [2] Kołodziej M., Majkowski A., Rak R.J.: 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. Part I, Book Series: Lecture Notes in Computer Science, v. 6593 (2011), 280-289.
- [3] Wolpaw J.R., Birbaumer N., McFarland D.J., Pfurtscheller G., Vaughan T.M.: Brain-computer interfaces for communication and control. Clinical Neurophysiology, Jun 2002, 113(6), 767-791.
- [4] Wolpaw J., Winter Wolpaw E., Brain-Computer Interfaces: Principles and Practice. Oxford University Press, 2012.
- [5] Pascual-Marqui R.D.: Review of Methods for solving the EEG Inverse Problem. International Journal of Bioelectromagnetism, v.1 (1999), no. 1, p. 75-86.
- [6] Grech R., Cassar T., Muscat J., Camilleri K.P., Fabri S.G., Zervakis M., Xanthopoulos P., Sakkalis V. and Vanrumste B.: Review on solving the inverse problem in EEG source analysis. Journal of NeuroEngineering and Rehabilitation 2008, 5:25 (7 Nov. 2008).
- [7] Kandel E.R., Schwartz J.H., Jessell T.M., Siegelbaum S.A., Hudspeth A.J., Principles of Neural Science. McGraw-Hill Professional, 5th Edition, ISBN: 978-0071390118, 2012.
- [8] Kamousi B., Liu Z., He B., Classification of Motor Imagery Tasks for Brain-Computer Interface Applications by Means of Two Equivalent Dipoles Analysis. IEEE Transactions on Neuronal Systems and Rehabilitation Engineering, v. 13, no. 2, June 2005, 166-171.
- [9] Frolov A.A., Húsek D., Bobrov P.D., Korshakov A., Chernikova L., Konovalov R., Mokienko O., Localizing Sources of Brain Activity Relevant to Motor Imagery Brain-Computer Interface Performance, Using Individual Head Geometry. Advances in Neural Networks - ISNN 2012, v. 7367, 2012, 369-378.
- [10] Ferrez P.W., del R. Millán J., Error-Related EEG Potentials Generated During Simulated Brain–Computer Interaction. IEEE Transactions on Biomedical Engineering, v. 55, no. 3, March 2008, 923-929.
- [11] Noirhomme Q., Kitney R.I., Macq B., Single-Trial EEG Source Reconstruction for Brain–Computer Interface. IEEE Transactions on Biomedical Engineering, v. 55, no. 5, May 2008, 1592-1601.
- [12] Jagodzińska U., Towards the Applications of Algorithms for Inverse Solutions in EEG Brain-Computer Interfaces. International Journal of Electronics and Telecommunications, 2013, v. 59, no. 3, 277–283.
- [13] Painold A., Anderer P., Holl A.K., Letmaier M., Saletu-Zyhlarz G.M., Saletu B., Bonelli R.M.: EEG low-resolution brain electromagnetic tomography (LORETA) in Huntington’s disease. Journal of Neurology, 258 (2011), no. 5, 840-854.
- [14] http://www.bbci.de/competition/iii/, BCI Competition III; Data set V ‹mental imagery, multi-class›.
- [15] http://www.toiowonaukowo.blogspot.com/2012_10_01_archive.html, ‘Interfejs mózg-komputer. To i Owo Naukowo.’ – archiwum z października 2012 r.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-16fcb8f1-6e0c-43f7-9212-e8f9e176167b