An attempt to localize brain electrical activity sources using EEG with limited number of electrodes

• Autorzy: Majkowski A. , Oskwarek Ł. , Kołodziej M. , Rak R. J.

Identyfikatory

DOI

10.1016/j.bbe.2016.07.002

• Języki publikacji: EN

Abstrakty

EN

A very interesting research goal is to find underlying sources generating the EEG signal–referred to as the ‘‘EEG inverse problem’’. Its aim is to determine spatial distribution of brain activity, described by local brain currents density, on the basis of potentials measured on the scalp as EEG signal. The purpose of the research presented in the article was to check whether the results of the inverse problem solution, obtained by the LORETA algorithm for the reduced set of 8 electrodes selected by the authors will be close to the results for the initial set of 32 electrodes. EEG signals were registered during the BCI operation based on ERD/ERS potentials. Obtained results showed no significant differences in the location of the most important sources in both cases. It is worth emphasizing that reducing the number of electrodes would have a significant impact on an BCI ergonomics.

Słowa kluczowe

EN

LORETA; feature extraction; feature selection; EEG inverse problem; brain computer interface; BCI

PL

LORETA; ekstrakcja cech; selekcja cech; zagadnienie odwrotne EEG; interfejs mózg-komputer; BCI

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2016
• Tom: Vol. 36, no. 4
• Strony: 686--696
• Opis fizyczny: Bibliogr. 25 poz., rys., tab., wykr.

Twórcy

autor

Majkowski A.

• Warsaw University of Technology, Institute of the Theory of Electrical Engineering, Measurement and Information Systems, ul. Koszykowa 75, 00-662 Warsaw, Poland

autor

Oskwarek Ł.

• Warsaw University of Technology, Institute of the Theory of Electrical Engineering, Measurement and Information Systems, ul. Koszykowa 75, 00-662 Warsaw, Poland

autor

Kołodziej M.

• Warsaw University of Technology, Institute of the Theory of Electrical Engineering, Measurement and Information Systems, ul. Koszykowa 75, 00-662 Warsaw, Poland

autor

Rak R. J.

• Warsaw University of Technology, Institute of the Theory of Electrical Engineering, Measurement and Information Systems, ul. Koszykowa 75, 00-662 Warsaw, Poland

Bibliografia

• [1] Edlinger G, Prull A, Neuper C, Pfurtscheller G. High-resolution ERD: cortical imaging of event-related desynchronization during motor imagery. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1998, vol. 4; 1998. p. 2128–30.
• [2] Park S, Choi JH, Jung HK. Evaluation of features for electrode location robustness in brain–computer interface (BCI). 2012 Sixth International Conference on Electromagnetic Field Problems and Applications (ICEF). 2012. pp. 1–4.
• [3] Rak RJ, Kołodziej M, Majkowski A. Brain-computer interface as measurement and control system the review paper. Metrol Meas Syst 2012;19(3):427–44.
• [4] Pregenzer M, Pfurtscheller G, Flotzinger D. Selection of electrode positions for an EEG-based Brain Computer Interface (BCI). Auswahl von Elektrodenpositionen für ein auf EEG-Ableitungen basierendes Brain Computer Interface (BCI). Biomed Tech Eng 1994;39(10):264–9.
• [5] Grech R, Cassar T, Muscat J, Camilleri KP, Fabri SG, Zervakis M, Xanthopoulos P, Sakkalis V, Vanrumste B. Review on solving the inverse problem in EEG source analysis. J NeuroEng Rehabil 2008;5:25.
• [6] Huang Y, Dmochowski JP, Su Y, Datta A, Rorden C, Parra LC. Automated MRI segmentation for individualized modeling of current flow in the human head. J Neural Eng 2013; 10(6 (December)).
• [7] ‘‘Wiley: EEG Signal Processing – Saeid Sanei, J.A. Chambers.’’ Available at: http://eu.wiley.com/WileyCDA/WileyTitle/productCd- 0470025816.html [accessed 06.0516].
• [8] ‘‘EEG source localization: Sensor density and head surface coverage.’’ Available at: http://www.sciencedirect.com/science/article/pii/ S0165027015003064 [accessed: 06.05.16].
• [9] Baillet S, Riera JJ, Marin G, Mangin JF, Aubert J, Garnero L. Evaluation of inverse methods and head models for EEG source localization using a human skull phantom. Phys Med Biol 2001;46(1):77.
• [10] RD P-M. Review of methods for solving the EEG inverse problem. Int J Bioelectromagn 1999;1(1):75–86.
• [11] Millan JR. On the need for on-line learning in brain– computer interfaces. Proceedings of 2004 IEEE International Joint Conference on Neural Networks, 2004, vol. 4; 2004. p. 2877–82.
• [12] Schalk G, McFarland DJ, Hinterberger T, Birbaumer N, Wolpaw JR. BCI2000: a general-purpose brain–computer interface (BCI) system. IEEE Trans Biomed Eng 2004;51(6 (June)):1034–43.
• [13] Diedrichsen J, Balsters JH, Flavell J, Cussans E, Ramnani N. A probabilistic MR atlas of the human cerebellum. NeuroImage 2009;46(1 (May)):39–46.
• [14] ‘‘Co-Planar Stereotaxic Atlas of the Human Brain.’’ Available at: http://www.thieme.com/books-main/neurosurgery/ product/414-co-planar-stereotaxic-atlas- of-the-human-brain [accessed 06.05.16].
• [15] ‘‘BIC – The McConnell Brain Imaging Centre: Home Page.’’ Available at: http://www.bic.mni.mcgill.ca/ServicesAtlases/HomePage [accessed 06.05.16].
• [16] Lancaster JL, Tordesillas-Gutiérrez D, Martinez M, Salinas F, Evans A, Zilles K, et al. Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template. Hum Brain Mapp 2007;28(11 (November)):1194–205.
• [17] ‘‘Low resolution brain electromagnetic tomography, LORETA, sLORETA, eLORETA, by R.D. Pascual-Marqui.’’ Available at: http://www.uzh.ch/keyinst/loreta.htm [accessed: 06.05.16].
• [18] Laufs H, Kleinschmidt A, Beyerle A, Eger E, Salek-Haddadi A, Preibisch C, et al. EEG-correlated fMRI of human alpha activity. NeuroImage 2003;19(4 (August)): 1463–76.
• [19] McFarland DJ, Wolpaw JR. Sensorimotor rhythm-based brain-computer interface (BCI): model order selection for autoregressive spectral analysis. J Neural Eng 2008; 5(2 (June)):155–62.
• [20] Sherlin LH. Diagnosing and treating brain function through the use of low resolution brain electromagnetic tomography (LORETA). Introduction to quantitative EEG and neurofeedback. Elsevier; 2009. p. 83–102.
• [21] Michel CM, Murray MM, Lantz G, Gonzalez S, Spinelli L, de Peralta RG. EEG source imaging. Clin Neurophysiol 2004;115 (10 (October)):2195–222.
• [22] Mientus S, Gallinat J, Wuebben Y, Pascual-Marqui RD, Mulert C, Frick K, et al. Cortical hypoactivation during resting EEG in schizophrenics but not in depressives and schizotypal subjects as revealed by low resolution electromagnetic tomography (LORETA). Psychiatry Res 2002;116(1–2 (November)):95–111.
• [23] Herrmann MJ, Römmler J, Ehlis A-C, Heidrich A, Fallgatter AJ. Source localization (LORETA) of the error-related-negativity (ERN/Ne) and positivity (Pe). Brain Res Cogn Brain Res 2004;20(2 (July)):294–9.
• [24] Lantz G, Michel CM, Pascual-Marqui RD, Spinelli L, Seeck M, Seri S, et al. Extracranial localization of intracranial interictal epileptiform activity using LORETA (low resolution electromagnetic tomography). Electroencephalogr Clin Neurophysiol 1997; 102(5 (May)):414–22.
• [25] Schimpf PH, Liu H, Ramon C, Haueisen J. Efficient electromagnetic source imaging with adaptive standardized LORETA/FOCUSS. IEEE Trans Biomed Eng 2005;52(5 (May)):901–8.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.