Local spatial analysis of EEG signals using the Laplacian montage
https://doi.org/10.32362/2500-316X-2024-12-1-92-100
Abstract
Objectives. One pressing problem when recording brain activity signals by electroencephalography (EEG) is the need to reduce the effect of interference (artifacts). This study presents a method for resolving this problem using the Laplace differential operator. The aim is to determine the number of electrodes included in the Laplacian montage, as well as to clarify the requirements for the geometric shape of their placement, in order to ensure the best quality of EEG signal processing.
Methods. The Laplacian montage method is based on the use of individual electrodes to determine the second derivative of the signal, proportional to the electric current at the corresponding point on the surface of the head. This approach allows the potential of neural activity of the source located in a small area limited by the electrode complex to be evaluated. By using a small number of equidistant electrodes placed around the target electrode, the Laplacian montage can produce a significantly higher quality signal from the area under the electrode complex.
Results. Among all the methods for constructing the Laplacian montage discussed in the article, a complex consisting of 16 + 1 electrodes was shown to be preferable. The choice of the 16 + 1 scheme was determined by the best compromise between the quality of EEG signal processing and the complexity of manufacturing the electrode complex with given geometric parameters. The quality assessment was carried out by simulating the interference signal which allowed the correctness of the choice of installation design to be evaluated.
Conclusions. The use of the Laplacian montage method can significantly reduce the effect of artifacts. The proposed montage scheme ensures a good suppression of interference signals, the sources of which are located far beyond the projection of the electrode complex. However, not all interference arising from sources deep inside the brain, can be effectively suppressed using the Laplacian montage scheme alone.
About the Authors
A. A. SlezkinRussian Federation
Andrey A. Slezkin - Engineer, Laboratory of General and Clinical Neurophysiology, Institute of Higher Nervous Activity and Neurophysiology RAS ; Postgraduate Student, Department of Modeling of Radiophysical Processes, Institute of Radio Electronics and Informatics, MIREA – RTU.
5A, Butlerova ul., Moscow, 117485; 78, Vernadskogo pr., Moscow, 119454
Competing Interests:
The authors declare no conflicts of interest
S. P. Stepina
Russian Federation
Svetlana P. Stepina - Cand. Sci. (Phys.-Math.), Associate Professor, Scientific Educational Institute of Physical Research and Technology. Scopus Author ID 8937542900. ResearcherID E-7025-2018.
6, Miklukho-Maklaya ul., Moscow, 117198
Competing Interests:
The authors declare no conflicts of interest
N. G. Gusein-zade
Russian Federation
Namik G. Gusein-zade - Dr. Sci. (Phys.-Math.), Professor, Head of Department of Modeling of Radiophysical Processes, Institute of Radio Electronics and Informatics, MIREA – RTU; Chief Researcher of Theoretical Department, Prokhorov General Physics Institute of the RAS. Scopus Author ID 6506825772, ResearcherID S-7407-2016.
78, Vernadskogo pr., Moscow, 119454; 38, Vavilova ul., Moscow, 119991
Competing Interests:
The authors declare no conflicts of interest
References
1. Acharya J.N., Acharya V.J. Overview of EEG Montages and Principles of Localization. J. Clin. Neurophysiol. 2019;36(5): 325–329. https://doi.org/10.1097/wnp.0000000000000538
2. Tsuchimoto S., Shibusawa S., Iwama S., Hayashi M., Okuyama K., Mizuguchi N., Kato K., Ushiba J. Use of common average reference and large-Laplacian spatial-filters enhances EEG signal-to-noise ratios in intrinsic sensorimotor activity. J. Neurosci. Methods. 2021;353:109089. https://doi.org/10.1016/j.jneumeth.2021.109089
3. Gordon R., Rzempoluck E.J. Introduction to Laplacian Montages. Am. J. Electroneurodiagnostic Technol. 2004;44(2): 98–102. http://doi.org/10.1080/1086508X.2004.11079469
4. Hjorth B. An on-line transformation off EEG scalp potentials into orthogonal source derivations. Electroencephalogr. Clin. Neurophysiol. 1975;39(5):526–530. https://doi.org/10.1016/0013-4694(75)90056-5
5. Alzahrani S.I., Anderson C.W. A Comparison of Conventional and Tri-Polar EEG Electrodes for Decoding Real and Imaginary Finger Movements from One Hand. Int. J. Neural. Syst. 2021;31(9):2150036. https://doi.org/10.1142/s0129065721500362
6. Makeyev O., Ding Q., Besio W.G. Improving the accuracy of Laplacian estimation with novel multipolar concentric ring electrodes. Measurement (Lond). 2016;80:44–52. https://doi.org/10.1016/j.measurement.2015.11.017
7. Makeyev O. Solving the general inter-ring distances optimization problem for concentric ring electrodes to improve Laplacian estimation. BioMed. Eng. OnLine. 2018;17(1):117. https://doi.org/10.1186/s12938-018-0549-6
8. Dickey A.S., Alwaki A., Kheder A., Willie J.T., Drane D.L., Pedersen N.P. The Referential Montage Inadequately Localizes Corticocortical Evoked Potentials in Stereoelectroencephalography. J. Clin. Neurophysiol. 2022;39(5):412–418. https://doi.org/10.1097/wnp.0000000000000792
9. Carvalhaes C., de Barros J.A. The surface Laplacian technique in EEG: Theory and methods. Int. J. Psychophysiol. 2015;97(3):174–188. https://doi.org/10.1016/j.ijpsycho.2015.04.023
10. Greischar L.L., Burghy C.A., van Reekum C.M., Jackson D.C., Pizzagalli D.A., Mueller C., Davidson R.J. Effects of electrode density and electrolyte spreading in dense array electroencephalographic recording. Clin. Neurophysiol. 2004;115(3): 710–720. https://doi.org/10.1016/j.clinph.2003.10.028
11. Smith E.E., Bel-Bahar T.S., Kayser J. A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA. Psychophysiology. 2022;59(10):e14080. https://doi.org/10.1111/psyp.14080
12. Bufacchi R.J., Magri C., Novembre G., Iannetti G.D. Local spatial analysis: an easy-to-use adaptive spatial EEG filter. J. Neurophysiol. 2021;125(2):509–521. https://doi.org/10.1152/jn.00560.2019
13. Martin-Chinea K., Gomez-Gonzalez J.F., Acosta L. A New PLV-Spatial Filtering to Improve the Classification Performance in BCI Systems. IEEE Trans. Neural. Syst. Rehabil. Eng. 2022;30:2275–2282. https://doi.org/10.1109/tnsre.2022.3198021
14. Liu Q., Yang L., Zhang Z., Yang H., Zhang Y., Wu J. The Feature, Performance, and Prospect of Advanced Electrodes for Electroencephalogram. Biosensors (Basel). 2023;13(1):101. https://doi.org/10.3390/bios13010101
15. Ponomarev V.A., Pronina M.V., Kropotov Y.D. Dynamics of the EEG spectral density in the θ, α, and β bands in the visual Go/NoGo task. Hum. Physiol. 2017;43(4):366–376. https://doi.org/10.1134/S0362119717040132. [Original Russian Text: Ponomarev V.A., Pronina M.V., Kropotov Y.D. Dynamics of the EEG spectral density in the Theta, Alpha and Beta bands in the visual Go/NoGo task. Fiziologiya cheloveka. 2017;43(4):13–24 (in Russ.). https://doi.org/10.7868/S0131164617040130 ]
16. Poller B.V., Shchetinin Y.I., Orlov I.S. Adaptive digital filtering of signals in the systems of analysis of the electroencephalogram. Nauchnyi vestnik Novosibirskogo gosudarstvennogo tekhnicheskogo universiteta = Scientific Bulletin of NSTU. 2013;1(50):31–38 (in Russ.). Available from URL: https://elibrary.ru/PWURSR
Supplementary files
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1. Dummy head with a set of electrodes, implementing the Laplacian montage | |
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Type | Исследовательские инструменты | |
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Indexing metadata ▾ |
- One pressing problem when recording brain activity signals by electroencephalography is the need to reduce the effect of interference.
- The proposed Laplacian montage scheme ensures a good suppression of interference signals, the sources of which are located far beyond the projection of the electrode complex.
- A complex consisting of 16 + 1 electrodes was shown to be preferable.
Review
For citations:
Slezkin A.A., Stepina S.P., Gusein-zade N.G. Local spatial analysis of EEG signals using the Laplacian montage. Russian Technological Journal. 2024;12(1):92-100. https://doi.org/10.32362/2500-316X-2024-12-1-92-100