Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-17T18:06:36.746Z Has data issue: false hasContentIssue false

6 - High-Performance Electrophysiological Microsegmentation and Brain Source Localization

from Systemic Psychophysiology

Published online by Cambridge University Press:  27 January 2017

By
Stephanie Cacioppo
Edited by
John T. Cacioppo ,
Louis G. Tassinary  and
Gary G. Berntson
John T. Cacioppo
Affiliation:
University of Chicago
Louis G. Tassinary
Affiliation:
Texas A & M University
Gary G. Berntson
Affiliation:
Ohio State University
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Handbook of Psychophysiology , pp. 101 - 115
Publisher: Cambridge University Press
Print publication year: 2016

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Berger, H. (1929). Ueder das Elektroencephalogramm des Menschen. Archives für Psychiatry Nervenkrankheiten, 87: 527–570.Google Scholar
Brodbeck, V., Kuhn, A., von Wegner, F., Morzelewski, A. Tagliazucchi, E., Borisov, S., … & Laufs, H. (2012). EEG microstates of wakefulness and NREM sleep. NeuroImage, 62: 2129–2139.CrossRefGoogle ScholarPubMed
Brunet, D., Murray, M. M., & Michel, C. M. (2011). Spatio-temporal analysis of multichannel EEG: CARTOOL. Computational Intelligence and Neuroscience, 1: 813–870.Google Scholar
Button, K. S., Ioannidis, J. P., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S., & Munafò, M. R. (2013). Empirical evidence for low reproducibility indicates low pre-study odds. Nature Reviews Neuroscience, 14: 877.Google Scholar
Cacioppo, J. T. & Cacioppo, S. (2013). Minimal replicability, generalizability, and scientific advances in psychological science. European Journal of Personality, 27: 121–122.Google Scholar
Cacioppo, J. T. & Dorfman, D. D. (1987). Waveform moment analysis in psychophysiological research. Psychological Bulletin, 102: 421–438.Google Scholar
Cacioppo, J. T., Tassinary, L. G., & Berntson, G. G. (2000). Handbook of Psychophysiology, 2nd edn. Cambridge University Press.Google Scholar
Cacioppo, S., Balogh, S., & Cacioppo, J. T. (2015). Implicit attention to negative social, in contrast to nonsocial, words in the Stroop task differs between individuals high and low in loneliness: evidence from event-related brain microstates. Cortex, 70: 213–233.Google Scholar
Cacioppo, S., Banagee, M., Balogh, S., Cardenas-Iniguez, C., Qualter, P., & Cacioppo, J. T. (2016). Loneliness and implicit attention to social threat: a high performance electrical neuroimaging study. Cognitive Neuroscience, 7: 138–159.Google Scholar
Cacioppo, S., Bianchi-Demicheli, F., Bischof, P., Deziegler, E., Michel, C. M., & Landis, T. (2013a). Hemispheric specialization varies with EEG brain resting states and phase of menstrual cycle. PLoS One, 8: e63196.Google Scholar
Cacioppo, S. & Cacioppo, J. T. (2015). Dynamic spatiotemporal brain analyses using high-performance electrical neuroimaging: Part II. A step-by-step tutorial. Journal of Neuroscience Methods, 256: 184–197.Google Scholar
Cacioppo, S., Frum, C., Asp, E., Weiss, R. M., Lewis, L. W., & Cacioppo, J. T. (2013b). A quantitative meta-analysis of functional imaging studies of social rejection. Scientific Reports, 3: 2027.CrossRefGoogle ScholarPubMed
Cacioppo, S., Weiss, R. M., Runesha, H. B., & Cacioppo, J. T. (2014). Dynamic spatiotemporal brain analyses using high-performance electrical neuroimaging: theoretical framework and validation. Journal of Neuroscience Methods, 238: 11–34.CrossRefGoogle ScholarPubMed
Collura, T. F. (1993). History and evolution of electroencephalographic instruments and techniques. Journal of Clinical Neurophysiology, 10: 476–504.Google Scholar
Decety, J. & Cacioppo, S. (2012). The speed of morality: a high-density electrical neuroimaging study. Journal of Neurophysiology, 108: 3068–3072.Google Scholar
Delorme, A., Palmer, J., Onton, J., Oostenveld, R., & Makeig, S. (2012). Independent EEG sources are dipolar. PLoS One, 7: e30135.Google Scholar
Francis, G. (2014). The frequency of excess success for articles in Psychological Science. Psychonomic Bulletin & Review, 21, 1180–1187.Google Scholar
Gartner, M., Brodbeck, V., Helmut, L., & Schneider, G. (2015). A stochastic model for EEG microstate sequence analysis. NeuroImage, 104: 199–208.Google Scholar
Gloor, P. (1969). Hans Berger on the electroencephalogram of man. EEG Clinical Neurophysiology, Suppl. 28: 1–36.Google Scholar
Khanna, A., Pascual-Leone, A., Michel, C. M., & Farzan, F. (2015). Microstates in resting-state EEG: current status and future directions. Neuroscience & Biobehavioral Reviews, 49: 105–113.Google Scholar
Koenig, T., Prichep, L., Lehmann, D., Sosa, P. V., Braeker, E., Kleinlogel, H., … & John, E. R. (2002). Millisecond by millisecond, year by year: normative EEG microstates and developmental stages. NeuroImage, 1: 41–48.Google Scholar
Lehmann, D. (1987). Principles of spatial analysis. In Gevins, A. & Remond, A. (eds.), Handbook of Electroencephalography and Clinical Neurophysiology, Vol. 1: Methods of Analysis of Brain Electrical and Magnetic Signals (pp. 309–354). Amsterdam: Elsevier.Google Scholar
Lehmann, D. & Skrandies, W. (1980). Reference-free identification of components of checkerboard-evoked multichannel potential fields. Electroencephalography & Clinical Neurophysiology, 48: 609–621.Google Scholar
Lehmann, D. & Skrandies, W. (1984). Spatial analysis of evoked potentials in man: a review. Progress in Neurobiology, 23: 227–250.Google Scholar
Luck, S. J. (2014). An Introduction to the Event-Related Potential Technique. Cambridge, MA: MIT Press.Google Scholar
Luck, S. J. & Kappenman, E. S. (eds.) (2012). The Oxford Handbook of Event-Related Potential Components. New York: Oxford University Press.Google Scholar
Ortigue, S., Michel, C. M., Murray, M. M., Mohr, C., Carbonnel, S., & Landis, T. (2004). Electrical neuroimaging reveals early generator modulation to emotional words. NeuroImage, 21: 1242–1251.Google Scholar
Ortigue, S., Sinigaglia, C., Rizzolatti, G., & Grafton, S. T. (2010). Understanding actions of others: the electrodynamics of the left and right hemispheres – a high-density EEG neuroimaging study. PLoS One, 5: 13.Google Scholar
Ortigue, S., Thompson, J. C., Parasuraman, R., & Grafton, S. T. (2009). Spatio-temporal dynamics of human intention understanding in temporo-parietal cortex: a combined EEG/fMRI repetition suppression paradigm. PLoS One, 4: 6962.Google Scholar
Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1995). Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Transactions on Biomedical Engineering, 42: 658–665.CrossRefGoogle ScholarPubMed
Picton, T. W., Bentin, S., Berg, P., Donchin, E., Hillyard, S. A., Johnson, R. J., … & Taylor, M. J. (2000). Guidelines for using event-related potentials to study cognition: recording standards and publication criteria. Psychophysiology, 37: 127–152.Google Scholar
Sarter, M., Bernston, G. G., & Cacioppo, J. T. (1996). Brain imaging and cognitive neuroscience: toward strong inference in attributing function to structure. American Psychologist, 51: 13–21.Google Scholar
Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D., & Leahy, R. M. (2011). Brainstorm: a user-friendly application for MEG/EEG analysis. Computational Intelligence and Neuroscience, ID 879716.CrossRefGoogle Scholar
Volkmer, T., Tahaghoghi, S. M. M., & Williams, H. E. (2004). Gradual transition detection using average frame similarity. Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop, vol. 9 (p. 139). Washington, DC: IEEE Computer Society.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×