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Fundamentals of Electroencephalography and Magnetoencephalography

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Language Electrified

Part of the book series: Neuromethods ((NM,volume 202))

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Abstract

This chapter introduces and guides the readers to the second part of this book, starting with a short summary of basic principles of neurophysiology and moving to surveying the past, existing, and future advances of electroencephalography and magnetoencephalography applied to study language. In particular, we review the basic physics principles exploited for data acquisition, and briefly introduce the resulting main functional measures: evoked responses, brain oscillations, and connectivity. Furthermore, we discuss how novel approaches, namely, functional dynamic connectivity and multimodal imaging can foster knowledge on language-related neural functioning. Finally, we overview the frontiers of ecological and comprehensive MEEG settings that promise to optimally suit speech and language research.

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References

  1. Kandel ER, Siegelbaum SA (2008) Chapter 10: overview of synaptic transmission. In: Principles of neural science, 5th edn. McGraw Hill, New York

    Google Scholar 

  2. Millet D (2002) The origins of EEG - session VI – anatomical and physiological models and techniques. In: Seventh annual meeting of the International Society for the History of the Neurosciences

    Google Scholar 

  3. Haas LF (2003) Hans Berger (1873–1941), Richard Caton (1842–1926), and electroencephalography. J Neurol Neurosurg Psychiatry. https://doi.org/10.1136/jnnp.74.1.9

  4. Staley K (2004) NEUROSCIENCE: epileptic neurons go wireless. Science (80-. ). https://doi.org/10.1126/science.1101133

  5. Rapp M, Yarom Y, Segev I (1996) Modeling back propagating action potential in weakly excitable dendrites of neocortical pyramidal cells. Proc Natl Acad Sci U S A. https://doi.org/10.1073/pnas.93.21.11985

  6. Liu AK, Dale AM, Belliveau JW (2002) Monte Carlo simulation studies of EEG and MEG localization accuracy. Hum Brain Mapp. https://doi.org/10.1002/hbm.10024

  7. Cohen D, Halgren E (2003) Magnetoencephalography (Neuromagnetism). In: Encyclopedia of neuroscience. Elsevier

    Google Scholar 

  8. Manca AD, Di Russo F, Sigona F, Grimaldi M (2019) Electrophysiological evidence of phonemotopic representations of vowels in the primary and secondary auditory cortex. Cortex. https://doi.org/10.1016/j.cortex.2019.09.016

  9. Towle VL, Bolaños J, Suarez D, Tan K, Grzeszczuk R, Levin DN, Cakmur R, Frank SA, Spire JP (1993) The spatial location of EEG electrodes: locating the best-fitting sphere relative to cortical anatomy, Electroencephalogr. Clin Neurophysiol. https://doi.org/10.1016/0013-4694(93)90061-Y

  10. Ahonen AI, Hamalainen MS, Kajola MJ, Knuutila JET, Laine PP, Lounasmaa V, Parkkonen LT, Simola JT, Tesche CD (1993) 22-Channel SQUID instrument for investigating the magnetic signals from the human brain. Phys Scr 49:198

    Article  Google Scholar 

  11. Gross J, Baillet S, Barnes GR, Henson RN, Hillebrand A, Jensen O, Jerbi K, Litvak V, Maess B, Oostenveld R, Parkkonen L, Taylor JR, van Wassenhove V, Wibral M, Schoffelen JM (2013) Good practice for conducting and reporting MEG research. NeuroImage 65:349–363. https://doi.org/10.1016/j.neuroimage.2012.10.001

    Article  PubMed  Google Scholar 

  12. Takeda T, Okamoto M, Miyazaki T, Morita N, Katagiri K (2011) Helium Circulation System (HCS) for the MEG. In: Magnetoencephalography. https://doi.org/10.5772/28904

    Chapter  Google Scholar 

  13. Cohen D (1968) Magnetoencephalography: evidence of magnetic fields produced by alpha-rhythm currents. Science (80-. ). https://doi.org/10.1126/science.161.3843.784

  14. Cohen D (1972) Magnetoencephalography: detection of the brain’s electrical activity with a superconducting magnetometer. Science (80-. ). https://doi.org/10.1126/science.175.4022.664

  15. Buzsáki G, Anastassiou CA, Koch C (2012) The origin of extracellular fields and currents-EEG, ECoG, LFP and spikes. Nat Rev Neurosci. https://doi.org/10.1038/nrn3241

  16. Pesaran B, Vinck M, Einevoll GT, Sirota A, Fries P, Siegel M, Truccolo W, Schroeder CE, Srinivasan R (2018) Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation. Nat Neurosci. https://doi.org/10.1038/s41593-018-0171-8

  17. Cohen D, Cuffin BN (1983) Demonstration of useful differences between magnetoencephalogram and electroencephalogram, Electroencephalogr. Clin Neurophysiol. https://doi.org/10.1016/0013-4694(83)90005-6

  18. Vorwerk J, Cho JH, Rampp S, Hamer H, Knösche TR, Wolters CH (2014) A guideline for head volume conductor modeling in EEG and MEG. NeuroImage. https://doi.org/10.1016/j.neuroimage.2014.06.040

  19. Lopes da Silva F (2013) EEG and MEG: relevance to neuroscience. Neuron. https://doi.org/10.1016/j.neuron.2013.10.017

  20. Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV (1993) Magnetoencephalography theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys. https://doi.org/10.1103/RevModPhys.65.413

  21. Murakami S, Okada Y (2006) Contributions of principal neocortical neurons to magnetoencephalography and electroencephalography signals. J Physiol. https://doi.org/10.1113/jphysiol.2006.105379

  22. Galvani L (1791) De viribus electricitatis in motu muscularis commentarius. Bononiensi Sci Artium Inst Atque Acad Comment. https://doi.org/10.1097/00000441-195402000-00023

  23. Walter WG, Cooper R, Aldridge VJ, McCallum WC, Winter AL (1964) Contingent negative variation : an electric sign of sensori-motor association and expectancy in the human brain. Nature. https://doi.org/10.1038/203380a0

  24. Sutton S, Braren M, Zubin J, John ER (1965) Evoked-potential correlates of stimulus uncertainty. Science (80-. ). https://doi.org/10.1126/science.150.3700.1187

  25. Näätänen R, Gaillard AWK, Mäntysalo S (1978) Early selective-attention effect on evoked potential reinterpreted. Acta Psychol. https://doi.org/10.1016/0001-6918(78)90006-9

  26. Schmitt BM, Münte TF, Kutas M (2000) Electrophysiological estimates of the time course of semantic and phonological encoding during implicit picture naming. Psychophysiology 37:473–484. https://doi.org/10.1017/S0048577200981782

    Article  CAS  PubMed  Google Scholar 

  27. Buzsáki G, Draguhn A (2004) Neuronal olscillations in cortical networks. Science (80- ). https://doi.org/10.1126/science.1099745

  28. Fries P (2005) A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn Sci 9:474–480. https://doi.org/10.1016/j.tics.2005.08.011

    Article  PubMed  Google Scholar 

  29. Buzsáki G (2006) Rhythms of the brain - introduction. Rhythm Brain. https://doi.org/10.1093/acprof:oso/9780195301069.001.0001

  30. Buzsáki G, Logothetis N, Singer W (2013) Scaling brain size, keeping timing: evolutionary preservation of brain rhythms. Neuron. https://doi.org/10.1016/j.neuron.2013.10.002

  31. Wang XJ (2010) Neurophysiological and computational principles of cortical rhythms in cognition. Physiol Rev. https://doi.org/10.1152/physrev.00035.2008

  32. Lakatos P, Chen CM, O’Connell MN, Mills A, Schroeder CE (2007) Neuronal oscillations and multisensory interaction in primary auditory cortex. Neuron. https://doi.org/10.1016/j.neuron.2006.12.011

  33. Kayser C, Montemurro MA, Logothetis NK, Panzeri S (2009) Spike-phase coding boosts and stabilizes information carried by spatial and temporal spike patterns. Neuron. https://doi.org/10.1016/j.neuron.2009.01.008

  34. Cohen MX (2014) Analyzing neural time series data: theory and practice. MIT Press. https://doi.org/10.1017/CBO9781107415324.004

    Book  Google Scholar 

  35. Keitel A, Gross J (2016) Individual human brain areas can be identified from their characteristic spectral activation fingerprints. PLoS Biol 14:1–22. https://doi.org/10.1371/journal.pbio.1002498

    Article  Google Scholar 

  36. Shadlen MN, Newsome WT (1994) Noise, neural codes and cortical organization. Curr Opin Neurobiol. https://doi.org/10.1016/0959-4388(94)90059-0

  37. Fries P (2015) Rhythms for cognition: communication through Coherence. Neuron 88:220–235. https://doi.org/10.1016/j.neuron.2015.09.034

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Schroeder CE, Lakatos P (2009) The gamma oscillation: master or slave? Brain Topogr 22:24–26. https://doi.org/10.1007/s10548-009-0080-y

    Article  PubMed  Google Scholar 

  39. VanRullen R, Busch NA, Drewes J, Dubois J (2011) Ongoing EEG phase as a trial-by-trial predictor of perceptual and attentional variability. Front Psychol. https://doi.org/10.3389/fpsyg.2011.00060

  40. VanRullen R (2016) Perceptual Cycles. Trends Cogn Sci 20:723–735. https://doi.org/10.1016/j.tics.2016.07.006

    Article  PubMed  Google Scholar 

  41. Large EW, Jones MR (1999) The dynamics of attending: how people track time-varying events. Psychol Rev 106:119–159. https://doi.org/10.1037/0033-295X.106.1.119

    Article  Google Scholar 

  42. Lakatos P, Karmos G, Mehta AD, Ulbert I, Schroeder CE (2008) Entrainment of neuronal oscillations as a mechanism of attentional selection. Science (80-. ) 320:110–113. https://doi.org/10.1126/science.1154735

    Article  CAS  Google Scholar 

  43. Merker BH, Madison GS, Eckerdal P (2009) On the role and origin of isochrony in human rhythmic entrainment. Cortex. https://doi.org/10.1016/j.cortex.2008.06.011

  44. Giraud AL, Poeppel D (2012) Cortical oscillations and speech processing: emerging computational principles and operations. Nat Neurosci 15:511–517. https://doi.org/10.1038/nn.3063

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Doelling KB, Poeppel D (2015) Cortical entrainment to music and its modulation by expertise. Proc Natl Acad Sci U S A 112:E6233–E6242. https://doi.org/10.1073/pnas.1508431112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Pallesen KJ, Bailey CJ, Brattico E, Gjedde A, Palva JM, Palva S (2015) Experience drives synchronization: the phase and amplitude dynamics of neural oscillations to musical chords are differentially modulated by musical expertise. PLoS One. https://doi.org/10.1371/journal.pone.0134211

  47. Uhlhaas PJ, Singer W (2006) Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology. Neuron 52:155–168. https://doi.org/10.1016/j.neuron.2006.09.020

    Article  CAS  PubMed  Google Scholar 

  48. Uhlhaas PJ, Grent-’t-Jongl T, Gross J (2018) Magnetoencephalography and translational neuroscience in psychiatry. JAMA Psychiatry 75:969–971. https://doi.org/10.1001/jamapsychiatry.2018.0775

    Article  PubMed  Google Scholar 

  49. Cuffin BN, Cohen D (1977) Magnetic fields of a dipole in special volume conductor shapes. IEEE Trans Biomed Eng. https://doi.org/10.1109/TBME.1977.326145

  50. Baillet S, Mosher JC, Leahy RM (2001) Electromagnetic brain mapping. IEEE Signal Process Mag. https://doi.org/10.1109/79.962275

  51. Hämäläinen MS, Ilmoniemi RJ (1994) Interpreting magnetic fields of the brain: minimum norm estimates. Med Biol Eng Comput. https://doi.org/10.1007/BF02512476

  52. Hillebrand A, Singh KD, Holliday IE, Furlong PL, Barnes GR (2005) A new approach to neuroimaging with magnetoencephalography. Hum Brain Mapp. https://doi.org/10.1002/hbm.20102

  53. Laaksonen H, Kujala J, Salmelin R (2008) A method for spatiotemporal mapping of event-related modulation of cortical rhythmic activity. NeuroImage. https://doi.org/10.1016/j.neuroimage.2008.04.175

  54. Sekihara K, Nagarajan SS, Poeppel D, Marantz A, Miyashita Y (2001) Reconstructing spatio-temporal activities of neural sources using an MEG vector beamformer technique. IEEE Trans Biomed Eng. https://doi.org/10.1109/10.930901

  55. Mosher JC, Baillet S, Leahy RM (2003) Equivalence of linear approaches in bioelectromagnetic inverse solutions. In: IEEE Work Stat Signal Process Proc. https://doi.org/10.1109/SSP.2003.1289402

    Chapter  Google Scholar 

  56. Baillet S (2017) Magnetoencephalography for brain electrophysiology and imaging. Nat Neurosci. https://doi.org/10.1038/nn.4504

  57. Yuval-Greenberg S, Tomer O, Keren AS, Nelken I, Deouell LY (2008) Transient induced gamma-band response in EEG as a manifestation of miniature Saccades. Neuron 58:429–441. https://doi.org/10.1016/j.neuron.2008.03.027

    Article  CAS  PubMed  Google Scholar 

  58. Muthukumaraswamy SD (2013) High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations. Front Hum Neurosci. https://doi.org/10.3389/fnhum.2013.00138

  59. Aydin Ü, Vorwerk J, Dümpelmann M, Küpper P, Kugel H, Heers M, Wellmer J, Kellinghaus C, Haueisen J, Rampp S, Stefan H, Wolters CH (2015) Combined EEG/MEG can outperform single modality EEG or MEG source reconstruction in presurgical epilepsy diagnosis. PLoS One. https://doi.org/10.1371/journal.pone.0118753

  60. Hari R, Aittoniemi K, Järvinen ML, Katila T, Varpula T (1980) Auditory evoked transient and sustained magnetic fields of the human brain localization of neural generators. Exp Brain Res. https://doi.org/10.1007/BF00237543

  61. Hari R, Salmelin R, Tissari SO, Kajola M, Virsu V (1994) Visual stability during eyeblinks [9]. Nature. https://doi.org/10.1038/367121b0

  62. Salmelin R, Hari R (1994) Characterization of spontaneous MEG rhythms in healthy adults. Electroencephalogr Clin Neurophysiol. https://doi.org/10.1016/0013-4694(94)90187-2

  63. Ince RAA, Giordano BL, Kayser C, Rousselet GA, Gross J, Schyns PG (2017) A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula. Hum Brain Mapp. https://doi.org/10.1002/hbm.23471

  64. Park H, Ince RAA, Schyns PG, Thut G, Park H, Ince RAA, Schyns PG, Thut G, Gross J (2015) Frontal top-down signals increase coupling of auditory low-frequency oscillations to continuous speech in human listeners report frontal top-down signals increase coupling of auditory low-frequency oscillations to continuous speech in human listeners. Curr Biol 25:1649–1653. https://doi.org/10.1016/j.cub.2015.04.049

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Schoffelen JM, Oostenveld R, Fries P (2005) Neuronal coherence as a mechanism of effective corticospinal interaction. Science (80-. ) 308:111–113. https://doi.org/10.1126/science.1107027

    Article  CAS  Google Scholar 

  66. Kluger DS, Gross J (2020) Tidal volume and respiration phase modulate cortico-muscular communication. BioRxiv 2020.01.13.904524. https://doi.org/10.1101/2020.01.13.904524

  67. Polanía R, Nitsche MA, Korman C, Batsikadze G, Paulus W (2012) The importance of timing in segregated theta phase-coupling for cognitive performance. Curr Biol 22:1314–1318. https://doi.org/10.1016/j.cub.2012.05.021

    Article  CAS  PubMed  Google Scholar 

  68. Florin E, Baillet S (2015) The brain’s resting-state activity is shaped by synchronized cross-frequency coupling of neural oscillations. NeuroImage. https://doi.org/10.1016/j.neuroimage.2015.01.054

  69. Markov NT, Vezoli J, Chameau P, Falchier A, Quilodran R, Huissoud C, Lamy C, Misery P, Giroud P, Ullman S, Barone P, Dehay C, Knoblauch K, Kennedy H (2014) Anatomy of hierarchy: feedforward and feedback pathways in macaque visual cortex. J Comp Neurol. https://doi.org/10.1002/cne.23458

  70. Michalareas G, Vezoli J, van Pelt S, Schoffelen JM, Kennedy H, Fries P (2016) Alpha-Beta and Gamma rhythms subserve feedback and feedforward influences among human visual cortical areas. Neuron. https://doi.org/10.1016/j.neuron.2015.12.018

  71. Palva JM, Wang SH, Palva S, Zhigalov A, Monto S, Brookes MJ, Schoffelen JM, Jerbi K (2018) Ghost interactions in MEG/EEG source space: a note of caution on inter-areal coupling measures. NeuroImage. https://doi.org/10.1016/j.neuroimage.2018.02.032

  72. Schoffelen JM, Gross J (2009) Source connectivity analysis with MEG and EEG. Hum Brain Mapp. https://doi.org/10.1002/hbm.20745

  73. Bastos AM, Schoffelen JM (2016) A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Front Syst Neurosci. https://doi.org/10.3389/fnsys.2015.00175

  74. Friston K, Moran R, Seth AK (2013) Analysing connectivity with Granger causality and dynamic causal modelling. Curr Opin Neurobiol. https://doi.org/10.1016/j.conb.2012.11.010

  75. Symmonds M, Moran CH, Leite MI, Buckley C, Irani SR, Stephan KE, Friston KJ, Moran RJ (2018) Ion channels in EEG: isolating channel dysfunction in NMDA receptor antibody encephalitis. Brain. https://doi.org/10.1093/brain/awy107

  76. Sherman MA, Lee S, Law R, Haegens S, Thorn CA, Hämäläinen MS, Moore CI, Jones SR (2016) Neural mechanisms of transient neocortical beta rhythms: converging evidence from humans, computational modeling, monkeys, and mice. Proc Natl Acad Sci U S A. https://doi.org/10.1073/pnas.1604135113

  77. Salmelin R, Schnitzler A, Schmitz F, Freund HJ (2000) Single word reading in developmental stutterers and fluent speakers. Brain. https://doi.org/10.1093/brain/123.6.1184

  78. Mersov A, Cheyne D, Jobst C, De Nil L (2018) A preliminary study on the neural oscillatory characteristics of motor preparation prior to dys fl uent and fl uent utterances in adults who stutter. J Fluen Disord 55:145–155. https://doi.org/10.1016/j.jfludis.2017.05.003

    Article  Google Scholar 

  79. van Ede F, Quinn AJ, Woolrich MW, Nobre AC (2018) Neural oscillations: sustained rhythms or transient burst-events? Trends Neurosci 41:415–417. https://doi.org/10.1016/j.tins.2018.04.004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Vidaurre D, Abeysuriya R, Becker R, Quinn AJ, Alfaro-Almagro F, Smith SM, Woolrich MW (2018) Discovering dynamic brain networks from big data in rest and task. NeuroImage. https://doi.org/10.1016/j.neuroimage.2017.06.077

  81. Zich C, Woolrich MW, Becker R, Vidaurre D, Scholl J, Hinson EL, Josephs L, Braeutigam S, Quinn AJ, Stagg CJ (2018) Motor learning shapes temporal activity in human sensorimotor cortex. BioRxiv. https://doi.org/10.1101/345421

  82. Vidaurre D, Quinn AJ, Baker AP, Dupret D, Tejero-Cantero A, Woolrich MW (2016) Spectrally resolved fast transient brain states in electrophysiological data. NeuroImage. https://doi.org/10.1016/j.neuroimage.2015.11.047

  83. Quinn AJ, Vidaurre D, Abeysuriya R, Becker R, Nobre AC, Woolrich MW, Quinn AJ (2018) Task-evoked dynamic network analysis through hidden Markov Modeling. Front Neurosci 12:1–17. https://doi.org/10.3389/fnins.2018.00603

    Article  Google Scholar 

  84. Sharon D, Hämäläinen MS, Tootell RBH, Halgren E, Belliveau JW (2007) The advantage of combining MEG and EEG: comparison to fMRI in focally stimulated visual cortex. NeuroImage. https://doi.org/10.1016/j.neuroimage.2007.03.066

  85. Dalal SS, Baillet S, Adam C, Ducorps A, Schwartz D, Jerbi K, Bertrand O, Garnero L, Martinerie J, Lachaux JP (2009) Simultaneous MEG and intracranial EEG recordings during attentive reading. NeuroImage. https://doi.org/10.1016/j.neuroimage.2009.01.017

  86. Hirschmann J, Özkurt TE, Butz M, Homburger M, Elben S, Hartmann CJ, Vesper J, Wojtecki L, Schnitzler A (2011) Distinct oscillatory STN-cortical loops revealed by simultaneous MEG and local field potential recordings in patients with Parkinson’s disease. NeuroImage. https://doi.org/10.1016/j.neuroimage.2010.11.063

  87. Litvak V, Jha A, Eusebio A, Oostenveld R, Foltynie T, Limousin P, Zrinzo L, Hariz MI, Friston K, Brown P (2011) Resting oscillatory cortico-subthalamic connectivity in patients with Parkinson’s disease. Brain. https://doi.org/10.1093/brain/awq332

  88. Abbasi O, Hirschmann J, Storzer L, Özkurt TE, Elben S, Vesper J, Wojtecki L, Schmitz G, Schnitzler A, Butz M (2018) Unilateral deep brain stimulation suppresses alpha and beta oscillations in sensorimotor cortices. NeuroImage. https://doi.org/10.1016/j.neuroimage.2018.03.026

  89. Oswal A, Jha A, Neal S, Reid A, Bradbury D, Aston P, Limousin P, Foltynie T, Zrinzo L, Brown P, Litvak V (2016) Analysis of simultaneous MEG and intracranial LFP recordings during Deep Brain Stimulation: a protocol and experimental validation. J Neurosci Methods. https://doi.org/10.1016/j.jneumeth.2015.11.029

  90. Ilmoniemi RJ, Kičić D (2010) Methodology for combined TMS and EEG. Brain Topogr 22:233–248. https://doi.org/10.1007/s10548-009-0123-4

    Article  PubMed  Google Scholar 

  91. Ilmoniemi RJ, Virtanen J, Ruohonen J, Karhu J, Aronen HJ, Näätänen R, Katila T (1997) Neuronal responses to magnetic stimulation reveal cortical reactivity and connectivity. Neuroreport. https://doi.org/10.1097/00001756-199711100-00024

  92. Riecke L, Sack AT, Schroeder CE (2015) Endogenous Delta/theta sound-brain phase entrainment accelerates the buildup of auditory streaming. Curr Biol 25:3196–3201. https://doi.org/10.1016/j.cub.2015.10.045

    Article  CAS  PubMed  Google Scholar 

  93. Thut G, Veniero D, Romei V, Miniussi C, Schyns P, Gross J (2011) Rhythmic TMS causes local entrainment of natural oscillatory signatures. Curr Biol 21:1176–1185. https://doi.org/10.1016/j.cub.2011.05.049

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Sack AT, Linden DEJ (2003) Combining transcranial magnetic stimulation and functional imaging in cognitive brain research: possibilities and limitations. Brain Res Rev. https://doi.org/10.1016/S0165-0173(03)00191-7

  95. Sarasso S, Rosanova M, Casali AG, Casarotto S, Fecchio M, Boly M, Gosseries O, Tononi G, Laureys S, Massimini M (2014) Quantifying cortical EEG responses to TMS in (Un)consciousness. Clin EEG Neurosci 45:40–49. https://doi.org/10.1177/1550059413513723

    Article  PubMed  Google Scholar 

  96. Sauseng P, Feldheim JF, Freunberger R, Hummel FC (2011) Right prefrontalTMS disrupts interregional anticipatory EEG alpha activity during shifting of visuospatial attention. Front Psychol 2:1–9. https://doi.org/10.3389/fpsyg.2011.00241

    Article  Google Scholar 

  97. Miniussi C, Thut G (2010) Combining TMS and EEG offers new prospects in cognitive neuroscience. Brain Topogr 22:249–256. https://doi.org/10.1007/s10548-009-0083-8

    Article  PubMed  Google Scholar 

  98. Thut G, Miniussi C (2009) New insights into rhythmic brain activity from TMS-EEG studies. Trends Cogn Sci 13:182–189. https://doi.org/10.1016/j.tics.2009.01.004

    Article  PubMed  Google Scholar 

  99. Herring JD, Esterer S, Marshall TR, Jensen O, Bergmann TO (2019) Low-frequency alternating current stimulation rhythmically suppresses gamma-band oscillations and impairs perceptual performance. NeuroImage. https://doi.org/10.1016/j.neuroimage.2018.09.047

  100. Ruhnau P, Neuling T, Fuscá M, Herrmann CS, Demarchi G, Weisz N (2016) Eyes wide shut: transcranial alternating current stimulation drives alpha rhythm in a state dependent manner. Sci Rep. https://doi.org/10.1038/srep27138

  101. Haumann NT, Parkkonen L, Kliuchko M, Vuust P, Brattico E (2016) Comparing the performance of popular MEG/EEG artifact correction methods in an evoked-response study. Comput Intell Neurosci 2016:1–11. https://doi.org/10.1155/2016/7489108

    Article  Google Scholar 

  102. Dmochowski JP, Datta A, Bikson M, Su Y, Parra LC (2011) Optimized multi-electrode stimulation increases focality and intensity at target. J Neural Eng. https://doi.org/10.1088/1741-2560/8/4/046011

  103. Liu A, Vöröslakos M, Kronberg G, Henin S, Krause MR, Huang Y, Opitz A, Mehta A, Pack CC, Krekelberg B, Berényi A, Parra LC, Melloni L, Devinsky O, Buzsáki G (2018) Immediate neurophysiological effects of transcranial electrical stimulation. Nat Commun. https://doi.org/10.1038/s41467-018-07233-7

  104. Wagner S, Rampersad SM, Aydin Ü, Vorwerk J, Oostendorp TF, Neuling T, Herrmann CS, Stegeman DF, Wolters CH (2014) Investigation of tDCS volume conduction effects in a highly realistic head model. J Neural Eng. https://doi.org/10.1088/1741-2560/11/1/016002

  105. Wagner S, Burger M, Wolters CH (2016) An optimization approach for well-targeted transcranial direct current stimulation. SIAM J Appl Math. https://doi.org/10.1137/15M1026481

  106. Huang Y, Liu AA, Lafon B, Friedman D, Dayan M, Wang X, Bikson M, Doyle WK, Devinsky O, Parra LC (2017) Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation. elife. https://doi.org/10.7554/eLife.18834

  107. Peters JC, Reithler J, Schuhmann T, de Graaf T, Uludag K, Goebel R, Sack AT (2013) On the feasibility of concurrent human TMS-EEG-fMRI measurements. J Neurophysiol 109:1214–1227. https://doi.org/10.1152/jn.00071.2012

    Article  PubMed  Google Scholar 

  108. Peters JC, Reithler J, Graaf TAD, Schuhmann T, Goebel R, Sack AT (2020) Concurrent human TMS-EEG-fMRI enables monitoring of oscillatory brain state-dependent gating of cortico-subcortical network activity. Commun Biol. https://doi.org/10.1038/s42003-020-0764-0

  109. Hauk O, Weiss B (2020) The neuroscience of natural language processing. Lang Cogn Neurosci 35:541–542. https://doi.org/10.1080/23273798.2020.1761989

    Article  Google Scholar 

  110. Hari R, Himberg T, Nummenmaa L, Hämäläinen M, Parkkonen L (2013) Synchrony of brains and bodies during implicit interpersonal interaction. Trends Cogn Sci. https://doi.org/10.1016/j.tics.2013.01.003

  111. Dikker S, Wan L, Davidesco I, Kaggen L, Oostrik M, McClintock J, Rowland J, Michalareas G, Van Bavel JJ, Ding M, Poeppel D (2017) Brain-to-brain synchrony tracks real-world dynamic group interactions in the classroom. Curr Biol 27:1375–1380. https://doi.org/10.1016/j.cub.2017.04.002

    Article  CAS  PubMed  Google Scholar 

  112. Silbert LJ, Honey CJ, Simony E, Poeppel D, Hasson U (2014) Coupled neural systems underlie the production and comprehension of naturalistic narrative speech. Proc Natl Acad Sci U S A. https://doi.org/10.1073/pnas.1323812111

  113. Thiede A, Glerean E, Kujala T, Parkkonen L (2020) Atypical MEG inter-subject correlation during listening to continuous natural speech in dyslexia. NeuroImage. https://doi.org/10.1016/j.neuroimage.2020.116799

  114. Krigolson OE, Williams CC, Norton A, Hassall CD, Colino FL (2017) Choosing MUSE: validation of a low-cost, portable EEG system for ERP research. Front Neurosci. https://doi.org/10.3389/fnins.2017.00109

  115. Zhdanov A, Nurminen J, Baess P, Hirvenkari L, Jousmäki V, Mäkelä JP, Mandel A, Meronen L, Hari R, Parkkonen L (2015) An internet-based real-time audiovisual link for dual MEG recordings. PLoS One. https://doi.org/10.1371/journal.pone.0128485

  116. Goebel R, Sorger B, Kaiser J, Birbaumer N, Weiskopf N (2004) BOLD brain pong: self-regulation of local brain activity during synchronously scanned, interacting subjects. Abstract viewer/itinerary planner. Society of Neuroscience, Washington, DC

    Google Scholar 

  117. Ebert D, Hefter H, Binkofski F, Freund HJ (2002) Coordination between breathing and mental grouping of pianistic finger movements. Percept Mot Skills 95:339–353. https://doi.org/10.2466/pms.2002.95.2.339

    Article  PubMed  Google Scholar 

  118. Heck DH, McAfee SS, Liu Y, Babajani-Feremi A, Rezaie R, Freeman WJ, Wheless JW, Papanicolaou AC, Ruszinkó M, Sokolov Y, Kozma R (2017) Breathing as a fundamental rhythm of brain function. Front Neural Circuits 10:1–8. https://doi.org/10.3389/fncir.2016.00115

    Article  Google Scholar 

  119. Ito J, Roy S, Liu Y, Cao Y, Fletcher M, Lu L, Boughter JD, Grün S, Heck DH (2014) Whisker barrel cortex delta oscillations and gamma power in the awake mouse are linked to respiration. Nat Commun 5:1–10. https://doi.org/10.1038/ncomms4572

    Article  CAS  Google Scholar 

  120. Nakamura NH, Fukunaga M, Oku Y (2018) Respiratory modulation of cognitive performance during the retrieval process. PLoS One 13:1–10. https://doi.org/10.1371/journal.pone.0204021

    Article  CAS  Google Scholar 

  121. Monti A, Porciello G, Tieri G, Aglioti SM (2019) The “embreathment” illusion highlights the role of breathing in corporeal awareness. J Neurophysiol:420–427. https://doi.org/10.1152/jn.00617.2019

  122. Critchley HD, Garfinkel SN (2018) The influence of physiological signals on cognition. Curr Opin Behav Sci. https://doi.org/10.1016/j.cobeha.2017.08.014

  123. Park H-D, Correia S, Ducorps A, Tallon-Baudry C (2014) Spontaneous fluctuations in neural responses to heartbeats predict visual detection. Nat Neurosci 17:612–618. https://doi.org/10.1038/nn.3671

    Article  CAS  PubMed  Google Scholar 

  124. Saxon SA (1970) Detection of near threshold signals during four phases of cardiac cycle. Ala J Med Sci 7:427

    CAS  PubMed  Google Scholar 

  125. Sandman CA, McCanne TR, Kaiser DN, Diamond B (1977) Heart rate and cardiac phase influences on visual perception. J Comp Physiol Psychol. https://doi.org/10.1037/h0077302

  126. Walker BB, Sandman CA (1982) Visual evoked potentials change as heart rate and carotid pressure change. Psychophysiology. https://doi.org/10.1111/j.1469-8986.1982.tb02579.x

  127. Elliott R, Graf V (1972) Visual sensitivity as a function of phase of cardiac cycle. Psychophysiology. https://doi.org/10.1111/j.1469-8986.1972.tb03219.x

  128. Haas F, Distenfeld S, Axen K (1986) Effects of perceived musical rhythm on respiratory pattern. J Appl Physiol 61:1185–1191. https://doi.org/10.1152/jappl.1986.61.3.1185

    Article  CAS  PubMed  Google Scholar 

  129. Varga S, Heck DH (2017) Rhythms of the body, rhythms of the brain: respiration, neural oscillations, and embodied cognition. Conscious Cogn 56:77–90. https://doi.org/10.1016/J.CONCOG.2017.09.008

    Article  PubMed  Google Scholar 

  130. Klimesch W (2018) The frequency architecture of brain and brain body oscillations: an analysis. Eur J Neurosci 48:2431–2453. https://doi.org/10.1111/ejn.14192

    Article  PubMed  PubMed Central  Google Scholar 

  131. Perl O, Ravia A, Rubinson M, Eisen A, Soroka T, Mor N, Secundo L, Sobel N (2019) Human non-olfactory cognition phase-locked with inhalation. Nat Hum Behav. https://doi.org/10.1038/s41562-019-0556-z

  132. Park HD, Barnoud C, Trang H, Kannape OA, Schaller K, Blanke O (2020) Breathing is coupled with voluntary action and the cortical readiness potential. Nat Commun 11. https://doi.org/10.1038/s41467-019-13967-9

  133. Zelano C, Jiang H, Zhou G, Arora N, Schuele S, Rosenow J, Gottfried JA (2016) Nasal respiration entrains human limbic oscillations and modulates cognitive function. J Neurosci 36:12448–12467. https://doi.org/10.1523/JNEUROSCI.2586-16.2016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Li S, Laskin JJ (2006) Influences of ventilation on maximal isometric force of the finger flexors. Muscle Nerve. https://doi.org/10.1002/mus.20592

  135. Al E, Iliopoulos F, Forschack N, Nierhaus T, Grund M, Motyka P, Gaebler M, Nikulin VV, Villringer A (2020) Heart-brain interactions shape somatosensory perception and evoked potentials. Proc Natl Acad Sci U S A 117:10575–10584. https://doi.org/10.1073/pnas.1915629117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Sandman CA (1984) Augmentation of the auditory event related potentials of the brain during diastole. Int J Psychophysiol. https://doi.org/10.1016/0167-8760(84)90004-7

  137. Schandry R, Sparrer B, Weitkunat R (1986) From the heart to the brain: a study of heartbeat contingent scalp potentials. Int J Neurosci. https://doi.org/10.3109/00207458608985677

  138. Pollatos O, Schandry R (2004) Accuracy of heartbeat perception is reflected in the amplitude of the heartbeat-evoked brain potential. Psychophysiology. https://doi.org/10.1111/1469-8986.2004.00170.x

  139. Birren JE, Cardon PV, Phillips SL (1963) Reaction time as a function of the cardiac cycle in young adults. Science (80-. ). https://doi.org/10.1126/science.140.3563.195-a

  140. Callaway E, Layne RS (1964) Interaction between the visual evoked response and two spontaneous biological rhythms: the EEG alpha cycle and the cardiac arousal cycle. Ann N Y Acad Sci. https://doi.org/10.1111/j.1749-6632.1964.tb26762.x

  141. Craig AD (2003) Interoception: the sense of the physiological condition of the body. Curr Opin Neurobiol. https://doi.org/10.1016/S0959-4388(03)00090-4

  142. Mayer EA (2011) Gut feelings: the emerging biology of gut–brain communication. Nat Rev Neurosci 12:453–466. https://doi.org/10.1038/nrn3071

    Article  CAS  PubMed  Google Scholar 

  143. Blanke O (2012) Multisensory brain mechanisms of bodily self-consciousness. Nat Rev Neurosci 13:556–571. https://doi.org/10.1038/nrn3292

    Article  CAS  PubMed  Google Scholar 

  144. Park HD, Tallon-Baudry C (2014) The neural subjective frame: from bodily signals to perceptual consciousness. Philos Trans R Soc B Biol Sci. https://doi.org/10.1098/rstb.2013.0208

  145. Alem O, Mhaskar R, Jiménez-Martínez R, Sheng D, LeBlanc J, Trahms L, Sander T, Kitching J, Knappe S (2017) Magnetic field imaging with microfabricated optically-pumped magnetometers. Opt Express. https://doi.org/10.1364/oe.25.007849

  146. Boto E, Holmes N, Leggett J, Roberts G, Shah V, Meyer SS, Muñoz LD, Mullinger KJ, Tierney TM, Bestmann S, Barnes GR, Bowtell R, Brookes MJ (2018) Moving magnetoencephalography towards real-world applications with a wearable system. Nature. https://doi.org/10.1038/nature26147

  147. Barraza P, Dumas G, Liu H, Blanco-Gomez G, van den Heuvel MI, Baart M, Pérez A (2019) Implementing EEG hyperscanning setups. MethodsX 6:428–436. https://doi.org/10.1016/j.mex.2019.02.021

    Article  PubMed  PubMed Central  Google Scholar 

  148. Dikker S, Silbert LJ, Hasson U, Zevin JD (2014) On the same wavelength: predictable language enhances speaker-listener brain-to-brain synchrony in posterior superior temporal gyrus. J Neurosci 34:6267–6272. https://doi.org/10.1523/JNEUROSCI.3796-13.2014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Dumas G, Fairhurst MT (2019) Reciprocity and alignment: quantifying coupling in dynamic interactions. PsyArXiv Prepr 8:1–30

    Google Scholar 

  150. Pérez A, Dumas G, Karadag M, Duñabeitia JA (2019) Differential brain-to-brain entrainment while speaking and listening in native and foreign languages. Cortex 111:303–315. https://doi.org/10.1016/j.cortex.2018.11.026

    Article  PubMed  Google Scholar 

  151. Bigos KL, Weinberger DR (2010) Imaging genetics—days of future past. NeuroImage 53(3):804–809

    Article  CAS  PubMed  Google Scholar 

  152. Medland SE, Grasby KL, Jahanshad N, Painter JN, Colodro-Conde L, Bralten J, Hibar DP, Lind PA, Pizzagalli F, Thomopoulos SI, Stein JL, Franke B, Martin NG, Thompson PM, ENIGMA Genetics Working Group (2022) Ten years of enhancing neuro-imaging genetics through meta-analysis: an overview from the ENIGMA genetics working group. Hum Brain Mapp 43(1):292–299

    Article  PubMed  Google Scholar 

  153. Watkins KE, Gadian DG, Vargha-Khadem F (1999) Functional and structural brain abnormalities associated with a genetic disorder of speech and language. Am J Hum Genet 65(5):1215–1221

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Pinel P, Fauchereau F, Moreno A, Barbot A, Lathrop M, Zelenika D, Le Bihan D, Poline J-B, Bourgeron T, Dehaene S (2012) Genetic variants of FOXP2 and KIAA0319/TTRAP/THEM2 locus are associated with altered brain activation in distinct language-related regions. J Neurosci 32(3):817–825

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Department of Neuropsychology & Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands

    Antonio Criscuolo

  2. Center for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University & Royal Academy of Music Aarhus/Aalborg, Aarhus C, Denmark

    Elvira Brattico

  3. Department of Education, Psychology, Communication, University of Bari Aldo Moro, Bari, Italy

    Elvira Brattico

Authors
  1. Antonio Criscuolo
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  2. Elvira Brattico
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Correspondence to Elvira Brattico .

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Editors and Affiliations

  1. Centro di Ricerca Interdisciplinare sul Linguaggio (CRIL), University of Salento, Lecce, Italy

    Mirko Grimaldi

  2. Department of Clinical Medicine, Aarhus University, Aarhus, Denmark

    Elvira Brattico

  3. Center of Functionally Integrative Neuroscience (CFIN), Department of Clinical Medicine, Aarhus University, Aarhus, Denmark

    Yury Shtyrov

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Criscuolo, A., Brattico, E. (2023). Fundamentals of Electroencephalography and Magnetoencephalography. In: Grimaldi, M., Brattico, E., Shtyrov, Y. (eds) Language Electrified. Neuromethods, vol 202. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3263-5_6

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