References
- Elektroentsefalografiia (Electroencephalography): A Guide. Ed. Aleksandrov M.V. St. Petersburg, 2020. 224 p. (in Russ.).
- Tatum U.O., Khusein A.M., Bendbadis S.R., Kaplan P.V. Klinicheskaia interpretatsiia elektroentsefalografii (Clinical interpretation of electroencephalography). Moscow, 2025. 264 p. (in Russ.).
- Gnezditskii V.V. Obratnaia zadacha EEG i klinicheskaia elektroentsefalografiia (Inverse EEG problem and clinical electroencephalography). 2004. 624 p. (in Russ.).
- Fuchs M., Wagner M., Kastner J. Boundary element method volume conductor models for EEG source reconstruction. Clin. Neurophysiology. 2001;112:1400–1407. doi: 10.1016/S1388-2457(01)00589-2
- Hämaälaäinen M., Hari R., Ilmoniemi R.J., Knuutila J., Lounasmaa O.V. Magnetoencephalography – Theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev. Mod. Phys. 1993;65(2):413–497. doi: 10.1103/RevModPhys.65.413
- Llinás R.R., Ribary U., Jeanmonod D., Kronberg E., Mitra P.P. Thalamocortical dysrhythmia: A neurological and neuropsychiatric syndrome characterized by magnetoencephalography. Proc. Natl. Acad. Sci. USA. 1999;96(26):15222–15227. doi: 10.1073/pnas.96.26.15222
- Magnetism in medicine: a handbook. Eds. Andra W., Nowak H. Wiley-VCH, 2007. 630 p.
- Baillet S. Magnetoencephalography for brain electrophysiology and imaging. Nature Neuroscience. 2017;20(3):327–339. doi: 10.1038/nn.4504
- Handbook of EEG Interpretation. Ed. Tatum IV W.O. Springer Publishing, 2021 doi: 10.1891/9780826147097
- Frigo M., Johnson S.G. The Design and Implementation of FFTW3. Proceedings of the IEEE. 2005;93(2):216–231. doi: 10.1109/JPROC.2004.840301
- Ustinin M., Boyko A., Rykunov S. Healthy aging changes in conventional frequency bands of neuroelectric brain activity reconstructed from resting-state MEG. Geroscience. 2025;47(3):4093–4108. doi: 10.1007/s11357-025-01522-y
- Little S., Bonaiuto J., Meyer S.S., Lopez J., Bestmann S., Barnes G. Quantifying the performance of MEG source reconstruction using resting state data. Neuroimage. 2018;181:453–460. doi: 10.1016/j.neuroimage.2018.07.030
- Hämäläinen M.S., Ilmoniemi R.J. Interpreting magnetic fields of the brain: minimum norm estimates. Med. Biol. Eng. Comput. 1994;32(1):35–42. doi: 10.1007/BF02512476
- Pascual-Marqui R.D. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods and findings in experimental and clinical pharmacology. 2002;24(Suppl D):5–12.
- Belardinelli P., Ortiz E., Barnes G., Noppeney U., Preissl H. Source reconstruction accuracy of MEG and EEG Bayesian inversion approaches. PLoS One. 2012;7(12). Article No. e51985. doi: 10.1371/journal.pone.0051985
- Friston K., Harrison L., Daunizeau J., Kiebel S., Phillips C., Trujillo-Barreto N., Henson R., Flandin G., Mattout J. Multiple sparse priors for the M/EEG inverse problem. Neuroimage. 2008;39(3):1104–1120. doi: 10.1016/j.neuroimage.2007.09.048
- Llinás R.R., Ustinin M.N. Frequency-pattern functional tomography of magnetoencephalography data allows new approach to the study of human brain organization. Front. Neural Circuits. 2014;8. Article No. 43. doi: 10.3389/fncir.2014.00043
- Llinás R.R., Ustinin M.N., Rykunov S.D., Boyko A.I., Sychev V.V., Walton K.D., Rabello G.M., Garcia J. Reconstruction of human brain spontaneous activity based on frequency-pattern analysis of magnetoencephalography data. Front. Neurosci. 2015;9. Article No. 373. doi: 10.3389/fnins.2015.00373
- Ustinin, M.N., Boyko, A.I. & Rykunov, S.D. Functional Tomography of Complex Systems Using Spectral Analysis of Multichannel Measurement Data. Pattern Recognit. Image Anal. 2023;33:1344–1374. doi: 10.1134/S1054661823040491
- Llinás R.R., Rykunov S., Walton K.D., Boyko A., Ustinin M. Splitting of the magnetic encephalogram into «brain» and «non-brain» physiological signals based on the joint analysis of frequency-pattern functional tomograms and magnetic resonance images. Frontiers in Neural Circuits. 2022;16. Article No. 834434. doi: 10.3389/fncir.2022.834434
- Rykunov S.D., Boyko A.I., Ustinin M.N. Reconstruction of the Electrical Structure of the Human Body Using Spectral Functional Tomography. Pattern Recognit. Image Anal. 2023;33:1315–1343. doi: 10.1134/S1054661823040387
- Niso G., Rogers C., Moreau J.T., Chen L.Y., Madjar C., Das S., Bock E., Tadel F., Evans A., Jolicoeur P., Baillet S. OMEGA: The Open MEG Archive. Neuroimage. 2015;124:1182–1187. doi: 10.1016/j.neuroimage.2015.04.028
- Coffey E.B.J., Herholz S.C., Scala S., Zatorre R.J. Montreal Music History Questionnaire: a tool for the assessment of music-related experience. In: The Neurosciences and Music IV: Learning and Memory Conference. Edinburgh, UK, 2011.
- Ustinin M.N., Sychev V.V., Walton K.D., Llinás R.R. New Methodology for the Analysis and Representation of Human Brain Function: MEGMRIAn. Mathematical Biology and Bioinformatics. 2014;9(2):464–481. doi: 10.17537/2014.9.464
- McCubbin J., Vrba J., Spear P., McKenzie D., Willis R., Loewen R., Robinson S.E., Fife A.A. Advanced electronics for the CTF MEG system. Neurol. Clin. Neurophysiol. 2004;2004:69.
- Taulu S., Kajola M. Presentation of electromagnetic multichannel data: The signal space separation method. Journal of Applied Physics. 2005;97(12). doi: 10.1063/1.1935742/893620
- Taulu S., Simola J. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Physics in Medicine & Biology. 2006;51(7):1759–1768. doi: 10.1088/0031-9155/51/7/008
- Gramfort A., Luessi M., Larson E., Engemann D.A., Strohmeier D., Brodbeck C., Goj R., Jas M., Brooks T., Parkkonen L., Hämäläinen M. MEG and EEG data analysis with MNE-Python. Front. Neurosci. 2013;7. Article No. 267. doi: 10.3389/fnins.2013.00267
- Frigo M., Johnson S.G. The Design and Implementation of FFTW3. Proceedings of the IEEE. 2005;93(2):216–231. doi: 10.1109/JPROC.2004.840301
- Oostenveld R., Fries P., Maris E., Schoffelen J.-M. FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Computational Intelligence and Neuroscience. 2011;2011(1). Article No. 156869. doi: 10.1155/2011/156869
- Sarvas J. Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. Phys. Med. Biol. 1987;32(1):11–22. doi: 10.1088/0031-9155/32/1/004
- Rykunov S.D., Rykunova E.D., Boyko A.I., Ustinin M.N. VirtEl - Software for Magnetic Encephalography Data Analysis by the Method of Virtual Electrodes. Mathematical Biology and Bioinformatics. 2019;14(1):340–354. doi: 10.17537/2019.14.340
- Rykunov S.D., Boyko A.I. Partial Spectroscopy of the Human Brain Based On the Joint Analysis of MEG and MRI Data. Application to the CamCAN Data Array. Ìàthematical Biology and Bioinformatics. 2025;20(1):135–146. doi: 10.17537/2025.20.135
- Capilla A., Arana L., García-Huéscar M., Melcón M., Gross J., Campo P. The natural frequencies of the resting human brain: An MEG-based atlas. NeuroImage. 2022;258. Article No. 119373. doi: 10.1016/j.neuroimage.2022.119373
|
|
|