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23 - EEG and ERP

from Part V - Physiological Measures

Published online by Cambridge University Press:  12 December 2024

By
Christian Panitz ,
Richard T. Ward ,
Jourdan Pouliot  and
Andreas Keil
Edited by
John E. Edlund  and
Austin Lee Nichols
John E. Edlund
Affiliation:
Rochester Institute of Technology, New York
Austin Lee Nichols
Affiliation:
Central European University, Vienna
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Summary

Electroencephalography (EEG) and its measures, such as event-related brain potentials (ERPs) and time-frequency analysis (TFA), are powerful tools for investigating cognitive and behavioral processes in humans and therefore are increasingly attracting attention in the social and behavioral sciences. This chapter has been written for readers who are interested in getting involved in EEG research or who may already have some experience and wish to expand their toolbox of EEG methods. It aims to address both needs by providing a brief overview of human electrophysiology, with new users in mind, followed by a discussion of common challenges and typical applications. We conclude by describing current trends and potential for future developments.

Type
Chapter
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The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
Volume 2: Performing Research
 , pp. 519 - 544
Publisher: Cambridge University Press
Print publication year: 2024

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References

Abiri, R., Borhani, S., Sellers, E. W., Jiang, Y., & Zhao, X. (2019). A comprehensive review of EEG-based brain–computer interface paradigms. Journal of Neural Engineering, 16(1), 011001. https://doi.org/10.1088/1741-2552/aaf12eCrossRefGoogle ScholarPubMed
Asadzadeh, S., Yousefi Rezaii, T., Beheshti, S., Delpak, A., & Meshgini, S. (2020). A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities. Journal of Neuroscience Methods, 339, 108740. https://doi.org/10.1016/j.jneumeth.2020.108740CrossRefGoogle ScholarPubMed
Babiloni, C., Barry, R. J., Başar, E., Blinowska, K. J., Cichocki, A., Drinkenburg, W. H. I. M., et al. (2020). International Federation of Clinical Neurophysiology (IFCN) – EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: Applications in clinical research studies. Clinical Neurophysiology, 131(1), 285–307. https://doi.org/10.1016/j.clinph.2019.06.234CrossRefGoogle ScholarPubMed
Bae, G.-Y., & Luck, S. J. (2018). Dissociable decoding of spatial attention and working memory from EEG oscillations and sustained potentials. Journal of Neuroscience, 38(2), 409–422. https://doi.org/10.1523/JNEUROSCI.2860-17.2017CrossRefGoogle ScholarPubMed
Berg, P., & Scherg, M. (1994). A multiple source approach to the correction of eye artifacts. Electroencephalography and Clinical Neurophysiology, 90(3), 229–241.CrossRefGoogle Scholar
Berger, H. (1929). Über das Elektrenkephalogramm des Menschen. Archiv für Psychiatrie und Nervenkrankheiten, 87, 527–570.CrossRefGoogle Scholar
Biasiucci, A., Franceschiello, B., & Murray, M. M. (2019). Electroencephalography. Current Biology, 29(3), R80–R85.CrossRefGoogle ScholarPubMed
Boudewyn, M. A., Luck, S. J., Farrens, J. L., & Kappenman, E. S. (2018). How many trials does it take to get a significant ERP effect? It depends. Psychophysiology, 55(6), e13049. https://doi.org/10.1111/psyp.13049CrossRefGoogle ScholarPubMed
Bressler, S. L., & Menon, V. (2010). Large-scale brain networks in cognition: Emerging methods and principles. Trends in Cognitive Sciences, 14(6), 277–290. https://doi.org/10.1016/j.tics.2010.04.004CrossRefGoogle ScholarPubMed
Bridwell, D. A., Cavanagh, J. F., Collins, A. G. E., Nunez, M. D., Srinivasan, R., Stober, S., & Calhoun, V. D. (2018). Moving beyond ERP components: A selective review of approaches to integrate EEG and behavior. Frontiers in Human Neuroscience, 12. https://doi.org/10.3389/fnhum.2018.00106CrossRefGoogle ScholarPubMed
Buzsáki, G., & Draguhn, A. (2004). Neuronal oscillations in cortical networks. Science, 304(5679), 1926–1929. https://doi.org/10.1126/science.1099745CrossRefGoogle ScholarPubMed
Cacioppo, J. T., Tassinary, L. G., & Berntson, G. (2007). Handbook of Psychophysiology. Cambridge University Press.Google Scholar
Chaumon, M., Bishop, D. V. M., & Busch, N. A. (2015). A practical guide to the selection of independent components of the electroencephalogram for artifact correction. Journal of Neuroscience Methods, 250, 47–63. https://doi.org/10.1016/j.jneumeth.2015.02.025CrossRefGoogle Scholar
Clayson, P. E., Keil, A., & Larson, M. J. (2022). Open science in human electrophysiology. International Journal of Psychophysiology, 174, 43–46. https://doi.org/10.1016/j.ijpsycho.2022.02.002CrossRefGoogle ScholarPubMed
Cohen, M. X. (2014). Analyzing Neural Time Series Data: Theory and Practice. MIT Press.CrossRefGoogle Scholar
Cook III, E. W., & Miller, G. A. (1992). Digital filtering: Background and tutorial for psychophysiologists. Psychophysiology, 29(3), 350–367. https://doi.org/10.1111/j.1469-8986.1992.tb01709.xCrossRefGoogle Scholar
Corby, J. C., Roth, W. T., & Kopell, B. S. (1974). Prevalence and methods of control of the cephalic skin potential EEG artifact. Psychophysiology, 11(3), 350–360. https://doi.org/10.1111/j.1469-8986.1974.tb00554.xCrossRefGoogle ScholarPubMed
Debener, S., Emkes, R., De Vos, M., & Bleichner, M. (2015). Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear. Scientific Reports, 5(1), 16743. https://doi.org/10.1038/srep16743CrossRefGoogle ScholarPubMed
Dirlich, G., Vogl, L., Plaschke, M., & Strian, F. (1997). Cardiac field effects on the EEG. Electroencephalography and Clinical Neurophysiology, 102(4), 307–315. https://doi.org/10.1016/S0013-4694(96)96506-2CrossRefGoogle ScholarPubMed
Donoghue, T., Haller, M., Peterson, E. J., Varma, P., Sebastian, P., Gao, R., et al. (2020). Parameterizing neural power spectra into periodic and aperiodic components. Nature Neuroscience, 23(12), 1655–1665. https://doi.org/10.1038/s41593-020-00744-xCrossRefGoogle Scholar
Freeman, W. J. (1975). Mass Action in the Nervous System: Examination of the Neurophysiological Basis of Adaptive Behavior through the EEG. Academic Press.Google Scholar
Gable, P., Miller, M., & Bernat, E. (2022). The Oxford Handbook of EEG Frequency. Oxford University Press.CrossRefGoogle Scholar
Galambos, R. (1992). A comparison of certain gamma-band (40 Hz) brain rhythms in cat and man. In Basar, E. & Bullock, T. (eds.), Induced Rhythms in the Brain (pp. 103–122). Springer.Google Scholar
Garrett-Ruffin, S., Hindash, A. C., Kaczkurkin, A. N., Mears, R. P., Morales, S., Paul, K., et al. (2021). Open science in psychophysiology: An overview of challenges and emerging solutions. International Journal of Psychophysiology, 162, 69–78. https://doi.org/10.1016/j.ijpsycho.2021.02.005CrossRefGoogle ScholarPubMed
Gibney, K. D., Kypriotakis, G., Cinciripini, P. M., Robinson, J. D., Minnix, J. A., & Versace, F. (2020). Estimating statistical power for event-related potential studies using the late positive potential. Psychophysiology, 57(2), e13482. https://doi.org/10.1111/psyp.13482CrossRefGoogle ScholarPubMed
Goncharova, I. I., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2003). EMG contamination of EEG: Spectral and topographical characteristics. Clinical Neurophysiology, 114(9), 1580–1593. https://doi.org/10.1016/S1388-2457(03)00093-2CrossRefGoogle ScholarPubMed
Gratton, G., Coles, M. G. H., & Donchin, E. (1983). A new method for off-line removal of ocular artifact. Electroencephalography and Clinical Neurophysiology, 55(4), 468–484. https://doi.org/10.1016/0013-4694(83)90135-9CrossRefGoogle ScholarPubMed
Hooker, J. M., & Carson, R. E. (2019). Human positron emission tomography neuroimaging. Annual Review of Biomedical Engineering, 21(1), 551–581. https://doi.org/10.1146/annurev-bioeng-062117-121056CrossRefGoogle ScholarPubMed
Huster, R. J., Debener, S., Eichele, T., & Herrmann, C. S. (2012). Methods for simultaneous EEG-fMRI: An introductory review. Journal of Neuroscience, 32(18), 6053–6060. https://doi.org/10.1523/JNEUROSCI.0447-12.2012CrossRefGoogle ScholarPubMed
Hyvärinen, A., & Oja, E. (2000). Independent component analysis: Algorithms and applications. Neural Networks, 13, 411–430. https://doi.org/10.1016/S0893-6080(00)00026-5CrossRefGoogle ScholarPubMed
Islam, M. K., Rastegarnia, A., & Yang, Z. (2016). Methods for artifact detection and removal from scalp EEG: A review. Neurophysiologie Clinique / Clinical Neurophysiology, 46(4–5), 287–305. https://doi.org/10.1016/j.neucli.2016.07.002CrossRefGoogle ScholarPubMed
Jackson, A. F., & Bolger, D. J. (2014). The neurophysiological bases of EEG and EEG measurement: A review for the rest of us. Psychophysiology, 51(11), 1061–1071. https://doi.org/10.1111/psyp.12283CrossRefGoogle ScholarPubMed
Jacobsen, N. S. J., Blum, S., Witt, K., & Debener, S. (2020). A walk in the park? Characterizing gait-related artifacts in mobile EEG recordings. European Journal of Neuroscience, 54(12), 8421–8440. https://doi.org/10.1111/ejn.14965CrossRefGoogle ScholarPubMed
Junghöfer, M., Elbert, T., Tucker, D. M., & Braun, C. (1999). The polar average reference effect: A bias in estimating the head surface integral in EEG recording. Clinical Neurophysiology, 110(6), 1149–1155. https://doi.org/10.1016/S1388-2457(99)00044-9CrossRefGoogle ScholarPubMed
Junghöfer, M., Elbert, T., Tucker, D. M., & Rockstroh, B. (2000). Statistical control of artifacts in dense array EEG/MEG studies. Psychophysiology, 37(4), 523–532. https://doi.org/10.1111/1469-8986.3740523CrossRefGoogle ScholarPubMed
Kappenman, E., Farrens, J., Zhang, W., Stewart, A. X., & Luck, S. J. (2021). ERP CORE: An open resource for human event-related potential research. NeuroImage, 225, 117465. https://doi.org/10.31234/osf.io/4azqmCrossRefGoogle ScholarPubMed
Kappenman, E. S., & Luck, S. J. (2012). ERP components: The ups and downs of brainwave recordings. In Luck, S. J. & Kappenman, E. S. (eds.), The Oxford Handbook of ERP Components. Oxford University Press.Google Scholar
Keil, A., Bernat, E. M., Cohen, M. X., Ding, M., Fabiani, M., Gratton, G., et al. (2022). Recommendations and publication guidelines for studies using frequency-domain and time-frequency-domain analyses of neural time series. Psychophysiology, 59(5), e14052. https://doi.org/10.1111/psyp.14052CrossRefGoogle ScholarPubMed
Keil, A., Debener, S., Gratton, G., Junghöfer, M., Kappenman, E. S., Luck, S. J., et al. (2014). Committee report: Publication guidelines and recommendations for studies using electroencephalography and magnetoencephalography. Psychophysiology, 51(1), 1–21. https://doi.org/10.1111/psyp.12147CrossRefGoogle ScholarPubMed
Klug, M., and Gramann, K. (2020). Identifying key factors for improving ICA-based decomposition of EEG data in mobile and stationary experiments. European Journal of Neuroscience, 54(12), 8406–8420. https://doi.org/10.1111/ejn.14992CrossRefGoogle ScholarPubMed
Lau-Zhu, A., Lau, M. P. H., & McLoughlin, G. (2019). Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges. Developmental Cognitive Neuroscience, 36, 100635. https://doi.org/10.1016/j.dcn.2019.100635CrossRefGoogle ScholarPubMed
Ledwidge, P., Foust, J., & Ramsey, A. (2018). Recommendations for developing an EEG laboratory at a primarily undergraduate institution. Journal of Undergraduate Neuroscience Education, 17(1), A10–A19.Google Scholar
Linden, D. E. (2005). The p300: Where in the brain is it produced and what does it tell us? Neuroscientist, 11(6), 563–576.CrossRefGoogle ScholarPubMed
Lins, O. G., Picton, T. W., Berg, P., & Scherg, M. (1993). Ocular artifacts in EEG and event-related potentials I: Scalp topography. Brain Topography, 6(1), 51–63. https://doi.org/10.1007/BF01234127CrossRefGoogle ScholarPubMed
Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI. Nature, 453(7197), 869–878. https://doi.org/10.1038/nature06976CrossRefGoogle Scholar
Luck, S. J. (2014). An Introduction to the Event-Related Potential Technique, 2nd ed. MIT Press.Google Scholar
Luck, S. J., & Kappenman, E. S. (2011). The Oxford Handbook of Event-Related Potential Components. Oxford University Press.Google Scholar
McMenamin, B. W., Shackman, A. J., Maxwell, J. S., Bachhuber, D. R. W., Koppenhaver, A. M., Greischar, L. L., & Davidson, R. J. (2010). Validation of ICA-based myogenic artifact correction for scalp and source-localized EEG. NeuroImage, 49(3), 2416–2432. https://doi.org/10.1016/j.neuroimage.2009.10.010CrossRefGoogle ScholarPubMed
Neymotin, S. A., Jacobs, K. M., Fenton, A. A., & Lytton, W. W. (2011). Synaptic information transfer in computer models of neocortical columns. Journal of Computational Neuroscience, 30(1), 69–84. https://doi.org/10.1007/s10827-010-0253-4CrossRefGoogle ScholarPubMed
Nunez, M. D., Vandekerckhove, J., & Srinivasan, R. (2017). How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters. Journal of Mathematical Psychology, 76, 117–130. https://doi.org/10.1016/j.jmp.2016.03.003CrossRefGoogle ScholarPubMed
Nunez, P. L., & Srinivasan, R. (2006). Electric Fields of the Brain, 2nd ed. Oxford University Press.CrossRefGoogle Scholar
Nunez, P. L., Srinivasan, R., Westdorp, A. F., Wijesinghe, R. S., Tucker, D. M., Silberstein, R. B., & Cadusch, P. J. (1997). EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. Electroencephalography and Clinical Neurophysiology, 103(5), 499–515.CrossRefGoogle ScholarPubMed
Olejniczak, P. (2006). Neurophysiologic basis of EEG. Journal of Clinical Neurophysiology, 23(3), 186–189. https://doi.org/10.1097/01.wnp.0000220079.61973.6cCrossRefGoogle ScholarPubMed
Pavlov, Y. G., Adamian, N., Appelhoff, S., Arvaneh, M., Benwell, C. S. Y., Beste, C., et al. (2021). #EEGManyLabs: Investigating the replicability of influential EEG experiments. Cortex, 144, 213–229. https://doi.org/10.1016/j.cortex.2021.03.013CrossRefGoogle ScholarPubMed
Perrin, F., Pernier, J., Bertrand, O., & Echallier, J. F. (1989). Spherical splines for scalp potential and current density mapping. Electroencephalography and Clinical Neurophysiology, 72(2), 184–187. https://doi.org/10.1016/0013-4694(89)90180-6CrossRefGoogle ScholarPubMed
Peyk, P., De Cesarei, A., & Junghöfer, M. (2011). Electromagnetic encephalography software: Overview and integration with other EEG/MEG toolboxes. Computational Intelligence and Neuroscience, 2011, 861705. https://doi.org/10.1155/2011/861705CrossRefGoogle ScholarPubMed
Picton, T. W., & Hillyard, S. A. (1972). Cephalic skin potentials in electroencephalography. Electroencephalography and Clinical Neurophysiology, 33, 419–424.CrossRefGoogle ScholarPubMed
Poldrack, R. A. (2011). Inferring mental states from neuroimaging data: From reverse inference to large-scale decoding. Neuron, 72(5), 692–697. https://doi.org/10.1016/j.neuron.2011.11.001CrossRefGoogle ScholarPubMed
Ray, W. J., & Cole, H. W. (1985). EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes. Science, 228(4700), 750–752.CrossRefGoogle ScholarPubMed
Riels, K., Ramos Campagnoli, R., Thigpen, N., & Keil, A. (2022). Oscillatory brain activity links experience to expectancy during associative learning. Psychophysiology, 59(5), e13946. https://doi.org/10.1111/psyp.13946CrossRefGoogle ScholarPubMed
Schlögl, A., Keinrath, C., Zimmermann, D., Scherer, R., Leeb, R., & Pfurtscheller, G. (2007). A fully automated correction method of EOG artifacts in EEG recordings. Clinical Neurophysiology, 118(1), 98–104. https://doi.org/10.1016/j.clinph.2006.09.003CrossRefGoogle ScholarPubMed
Sinnott-Armstrong, W., & Simmons, C. (2021). Some common fallacies in arguments from M/EEG data. NeuroImage, 245, 118725. https://doi.org/10.1016/j.neuroimage.2021.118725CrossRefGoogle ScholarPubMed
Sitaram, R., Ros, T., Stoeckel, L., Haller, S., Scharnowski, F., Lewis-Peacock, J., et al. (2017). Closed-loop brain training: The science of neurofeedback. Nature Reviews Neuroscience, 18(2), 86–100. https://doi.org/10.1038/nrn.2016.164CrossRefGoogle ScholarPubMed
Soong, A. C. K., Lind, J. C., Shaw, G. R., & Koles, Z. J. (1993). Systematic comparisons of interpolation techniques in topographic brain mapping. Electroencephalography and Clinical Neurophysiology, 87(4), 185–195. https://doi.org/10.1016/0013-4694(93)90018-QCrossRefGoogle ScholarPubMed
Tharwat, A. (2018). Independent component analysis: An introduction. Applied Computing and Informatics, 17(2), 222–249. https://doi.org/10.1016/j.aci.2018.08.006CrossRefGoogle Scholar
Thigpen, N. N., Kappenman, E. S., & Keil, A. (2017). Assessing the internal consistency of the event-related potential: An example analysis. Psychophysiology, 54(1), 123–138. https://doi.org/10.1111/psyp.12629CrossRefGoogle ScholarPubMed
Ullsperger, M., & Debener, S. (2010). Simultaneous EEG and fMRI: Recording, Analysis, and Application. Oxford University Press.CrossRefGoogle Scholar
Widmann, A., Schröger, E., & Maess, B. (2015). Digital filter design for electrophysiological data – a practical approach. Journal of Neuroscience Methods, 250, 34–46. https://doi.org/10.1016/j.jneumeth.2014.08.002CrossRefGoogle Scholar

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