Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
The article presents the possibility of using the method of imaging brain activity, LORETA LOw Resolution Electromagnetic TomogrAphy), that can base on electroencephalographical and magnetoencephalographical readings. Thanks to using the above-mentioned method, it is possible to localize the sources of the activity of individual signals registered on the head surface. This is very significant regarding construction of the brain-computer interfaces in order to conduct proper identification and classification of signals obtained during electroencephalography.
Słowa kluczowe
Wydawca
Czasopismo
Rocznik
Tom
Strony
262--264
Opis fizyczny
Bibliogr. 14 poz., rys., wzory
Twórcy
autor
- Department of Electrical, Control & Computer Engineering, Opole University of Technology, 76 Prószkowska St., PL-45-272 Opole, Poland
Bibliografia
- [1] Pascual-Marqui R. D., Michel C. M., Lehmann D.: Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. International Journal of Psychophysiology. 18, 1994, pp. 49-65.
- [2] Frei E., Gamma A., Pascual-Marqui R. D., Lehmann D., Hell D., Vollenwider F.: Localization of MDMA-induced brain activity in healthy volunteers using low resolution brain electromagnetic tomography (LORETA). Human Brain Mapping, 2001, pp. 152-165.
- [3] Paszkiel S.: Augmented reality of technological environment in correlation with brain computer interfaces for control processes, Advances in Intelligent Systems and Computing 267 - AISC, Springer, Conference Proceedings Citation Index (ISI Proceedings), Switzerland 2014, pp. 197-203.
- [4] Billiot K. M., Budzynski T. H., Andrasik F.: EEG patterns and chronic fatigue syndrome. Journal of Neurotherapy, 1997, pp.20-30.
- [5] Pascual-Marqui R. D.: Review of methods for solving the EEG inverse problem. International Journal of Bioelectromagnetism 1999, pp. 75-86.
- [6] Congedo M., Lotte F., Lécuyer A.: Classification of Movement Intention by Spatially Filtered Electromagnetic Inverse Solutions, Physics in Medicine and Biology, 2006, 51, pp. 1971-1989.
- [7] Lotte F., Lécuyer A., Arnaldi B.: An inverse Solution based Feature Extraction Algorithm using Fuzzy Set Theory for Brain-Computer Interfaces. IEEE Transactions on Signal Processing, 2009, 57, pp. 3253-3263.
- [8] Christian J. C., Morzorati S., Norton J. A., Williams C. J., O'Connor S., Li T. K.: Genetic analysis of the resting electroencephalographic power spectrum in human twins. Psychophysiology, 1996, pp. 584-591.
- [9] Fuchs M., Wagner M., Köhler T., Wischmann H. A.: Linear and Nonlinear Current Density Reconstructions. Journal of Clinical Neurophysiology, 1999, pp. 267-295.
- [10] Besserve M., Martinerie J., Garnero L.: Improving quantification of functional networks with EEG inverse problem. Evidence from a decoding point of view Neuroimage, 2011.
- [11] Pascual-Marqui R. D.: Standardized low resolution brain electromagnetic tomography (sLORETA): technical details. Methods & Findings in Experimental & Clinical Pharmacology 2002, pp. 5-12.
- [12] Lubar, J., Congedo, M., Askew, J. H.: Low-resolution electromagnetic tomography (LORETA) of cerebral activity in chronic depressive disorder. Int, J. Psychophysiol. 49 (3), 2003, pp. 175-185.
- [13] Collins D. L., Neelin P., Peters T. M., Evans A. C.: Automatic 3D intersubject registration of MR volumetric data into standardized Talairach space, Journal Comput. Assist. Tomogr., 1994, pp. 192-205.
- [14] Congedo M., Finos L., Turkheimer F.: A Multiple Hypothesis Test Procedure based on the sum of Test-Statistics. 10th Annual Meeting of the Organization for Human Brain Mapping, June 13-17, Budapest, Hungary, 2004, Abstract on CD.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5f6b5e92-9b01-4389-acef-e76b60a4a144