{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T05:36:40Z","timestamp":1782797800039,"version":"3.54.5"},"reference-count":60,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,9]],"date-time":"2020-12-09T00:00:00Z","timestamp":1607472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"JSPS","doi-asserted-by":"publisher","award":["Leading Graduate Schools of the Ministry of Education, Culture, Sports, Science and Technology (Japan)"],"award-info":[{"award-number":["Leading Graduate Schools of the Ministry of Education, Culture, Sports, Science and Technology (Japan)"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Netherlands Organization for Scientific Research","award":["NWA Startimpuls 400.17.602"],"award-info":[{"award-number":["NWA Startimpuls 400.17.602"]}]},{"name":"The Swartz Foundation","award":["Gift"],"award-info":[{"award-number":["Gift"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Magneto-\/Electro-encephalography (M\/EEG) commonly uses (fast) Fourier transformation to compute power spectral density (PSD). However, the resulting PSD plot lacks temporal information, making interpretation sometimes equivocal. For example, consider two different PSDs: a central parietal EEG PSD with twin peaks at 10 Hz and 20 Hz and a central parietal PSD with twin peaks at 10 Hz and 50 Hz. We can assume the first PSD shows a mu rhythm and the second harmonic; however, the latter PSD likely shows an alpha peak and an independent line noise. Without prior knowledge, however, the PSD alone cannot distinguish between the two cases. To address this limitation of PSD, we propose using cross-frequency power\u2013power coupling (PPC) as a post-processing of independent component (IC) analysis (ICA) to distinguish brain components from muscle and environmental artifact sources. We conclude that post-ICA PPC analysis could serve as a new data-driven EEG classifier in M\/EEG studies. For the reader\u2019s convenience, we offer a brief literature overview on the disparate use of PPC. The proposed cross-frequency power\u2013power coupling analysis toolbox (PowPowCAT) is a free, open-source toolbox, which works as an EEGLAB extension.<\/jats:p>","DOI":"10.3390\/s20247040","type":"journal-article","created":{"date-parts":[[2020,12,9]],"date-time":"2020-12-09T09:17:58Z","timestamp":1607505478000},"page":"7040","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Cross-Frequency Power-Power Coupling Analysis: A Useful Cross-Frequency Measure to Classify ICA-Decomposed EEG"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0487-2482","authenticated-orcid":false,"given":"Nattapong","family":"Thammasan","sequence":"first","affiliation":[{"name":"Human Media Interaction, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4062-6679","authenticated-orcid":false,"given":"Makoto","family":"Miyakoshi","sequence":"additional","affiliation":[{"name":"Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1300\/J184v04n03_05","article-title":"Comodulation: A new qEEG analysis metric for assessment of structural and functional disorders of the central nervous system","volume":"4","author":"Sterman","year":"2000","journal-title":"J. Neurother."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"52","DOI":"10.3389\/fncir.2020.00052","article-title":"Interactions Between Motor Thalamic Field Potentials and Single-Unit Spiking Are Correlated with Behavior in Rats","volume":"14","author":"Gaidica","year":"2020","journal-title":"Front. Neural Circuits"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1016\/j.neuron.2018.12.009","article-title":"Layer-Specific Physiological Features and Interlaminar Interactions in the Primary Visual Cortex of the Mouse","volume":"101","author":"Senzai","year":"2019","journal-title":"Neuron"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1126\/science.aan6203","article-title":"Learning-enhanced coupling between ripple oscillations in association cortices and hippocampus","volume":"358","author":"Khodagholy","year":"2017","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/j.neuron.2011.11.032","article-title":"Basal Ganglia Beta Oscillations Accompany Cue Utilization","volume":"73","author":"Leventhal","year":"2012","journal-title":"Neuron"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101692","DOI":"10.1016\/j.pneurobio.2019.101692","article-title":"The activity of the prelimbic cortex in rats is enhanced during the cooperative acquisition of an instrumental learning task","volume":"183","author":"Gruart","year":"2019","journal-title":"Prog. Neurobiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1016\/j.neuron.2016.03.036","article-title":"Network Homeostasis and State Dynamics of Neocortical Sleep","volume":"90","author":"Watson","year":"2016","journal-title":"Neuron"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Sheremet, A., Qin, Y., Kennedy, J.P., DiCola, N.M., and Maurer, A.P. (2019). High-order theta harmonics account for the detection of slow gamma. eNeuro, 6.","DOI":"10.1101\/428490"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/S0306-4522(02)00669-3","article-title":"Hippocampal network patterns of activity in the mouse","volume":"116","author":"Buhl","year":"2003","journal-title":"Neuroscience"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1146\/annurev-neuro-062111-150444","article-title":"Mechanisms of gamma oscillations","volume":"35","author":"Wang","year":"2012","journal-title":"Annu. Rev. Neurosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"11128","DOI":"10.1523\/JNEUROSCI.1327-10.2010","article-title":"Intrinsic circuit organization and theta\u2013gamma oscillation dynamics in the entorhinal cortex of the rat","volume":"30","author":"Quilichini","year":"2010","journal-title":"J. Neurosci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8605","DOI":"10.1523\/JNEUROSCI.0294-11.2011","article-title":"Relationships between hippocampal sharp waves, ripples, and fast gamma oscillation: Influence of dentate and entorhinal cortical activity","volume":"31","author":"Sullivan","year":"2011","journal-title":"J. Neurosci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1016\/j.neuron.2014.07.026","article-title":"Fear and safety engage competing patterns of theta-gamma coupling in the basolateral amygdala","volume":"83","author":"Stujenske","year":"2014","journal-title":"Neuron"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3026","DOI":"10.1523\/JNEUROSCI.3058-17.2018","article-title":"The Nucleus Reuniens Controls Long-Range Hippocampo\u2013Prefrontal Gamma Synchronization during Slow Oscillations","volume":"38","author":"Ferraris","year":"2018","journal-title":"J. Neurosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7054","DOI":"10.1073\/pnas.0911184107","article-title":"Bidirectional changes to hippocampal theta\u2013gamma comodulation predict memory for recent spatial episodes","volume":"107","author":"Shirvalkar","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1007\/BF02351033","article-title":"Amplitude and phase relationship between alpha and beta oscillations in the human electroencephalogram","volume":"43","author":"Carlqvist","year":"2005","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"15222","DOI":"10.1073\/pnas.96.26.15222","article-title":"Thalamocortical dysrhythmia: A neurological and neuropsychiatric syndrome characterized by magnetoencephalography","volume":"96","author":"Ribary","year":"1999","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mitra, P., and Bokil, H. (2007). Observed Brain Dynamics, Oxford University Press.","DOI":"10.1093\/acprof:oso\/9780195178081.001.0001"},{"key":"ref_19","unstructured":"Llin\u00e1s, R.R., Ribary, U., and Jeanmonod, D. (2004). Method and System for Diagnosing and Treating Thalamocortical Dysrhythmia. (6,687,525), U.S. Patent."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.jneumeth.2004.03.014","article-title":"Measuring fundamental frequencies in local field potentials","volume":"138","author":"Masimore","year":"2004","journal-title":"J. Neurosci. Methods"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Schultheiss, N.W., Schlecht, M., Jayachandran, M., Brooks, D.R., McGlothan, J.L., Guilarte, T.R., and Allen, T.A. (2020). Awake delta and theta-rhythmic hippocampal network modes during intermittent locomotor behaviors in the rat. Behav. Neurosci., in press.","DOI":"10.1101\/866962"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Sheremet, A., Qin, Y., Kennedy, J.P., DiCola, N.M., Burke, S.N., and Maurer, A.P. (2019). Methodological Considerations on the Use of Different Spectral Decomposition Algorithms to Study Hippocampal Rhythms. eNeuro, 6.","DOI":"10.1523\/ENEURO.0142-19.2019"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1152\/jn.00636.2018","article-title":"Theta-gamma cascades and running speed","volume":"121","author":"Sheremet","year":"2019","journal-title":"J. Neurophysiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.neuroimage.2018.05.054","article-title":"Dorsal and ventral cortices are coupled by cross-frequency interactions during working memory","volume":"178","author":"Popov","year":"2018","journal-title":"NeuroImage"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1162\/jocn_a_01190","article-title":"Language prediction is reflected by coupling between frontal gamma and posterior alpha oscillations","volume":"30","author":"Wang","year":"2018","journal-title":"J. Cogn. Neurosci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1162\/neco.1995.7.6.1129","article-title":"An information-maximization approach to blind separation and blind deconvolution","volume":"7","author":"Bell","year":"1995","journal-title":"Neural Comput."},{"key":"ref_27","unstructured":"Makeig, S., Bell, A.J., Jung, T.P., and Sejnowski, T.J. (1996). Independent component analysis of electroencephalographic data. Advances in Neural Information Processing Systems 8, MIT Press."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"10979","DOI":"10.1073\/pnas.94.20.10979","article-title":"Blind separation of auditory event-related brain responses into independent components","volume":"94","author":"Makeig","year":"1997","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1126\/science.1066168","article-title":"Dynamic brain sources of visual evoked responses","volume":"295","author":"Makeig","year":"2002","journal-title":"Science"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0079-6123(06)59007-7","article-title":"Information-based modeling of event-related brain dynamics: Why use ICA to decompose EEG\/MEG data?","volume":"159","author":"Onton","year":"2006","journal-title":"Prog. Brain Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1186\/1744-9081-7-30","article-title":"Automatic classification of artifactual ICA-components for artifact removal in EEG signals","volume":"7","author":"Winkler","year":"2011","journal-title":"Behav. Brain Funct."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"035013","DOI":"10.1088\/1741-2560\/11\/3\/035013","article-title":"Robust artifactual independent component classification for bci practitioners","volume":"11","author":"Winkler","year":"2014","journal-title":"J. Neural Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1111\/j.1469-8986.2010.01061.x","article-title":"Adjust: An automatic EEG artifact detector based on the joint use of spatial and temporal features","volume":"48","author":"Mognon","year":"2011","journal-title":"Psychophysiology"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.jneumeth.2010.07.015","article-title":"Faster: Fully automated statistical thresholding for EEG artifact rejection","volume":"192","author":"Nolan","year":"2010","journal-title":"J. Neurosci. Methods"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bigdely-Shamlo, N., Kreutz-Delgado, K., Kothe, C., and Makeig, S. (2013, January 3\u20137). Eyecatch: Data-mining over half a million EEG independent components to construct a fully-automated eye-component detector. Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBC, Osaka, Japan.","DOI":"10.1109\/EMBC.2013.6610881"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1111\/psyp.12290","article-title":"Classification of independent components of EEG into multiple artifact classes","volume":"52","author":"Andersen","year":"2015","journal-title":"Psychophysiology"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.jneumeth.2015.02.025","article-title":"A practical guide to the selection of independent components of the electroencephalogram for artifact correction","volume":"250","author":"Chaumon","year":"2015","journal-title":"J. Neurosci. Methods"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.neuroimage.2019.05.026","article-title":"ICLabel: An automated electroencephalographic independent component classifier, dataset, and website","volume":"198","author":"Makeig","year":"2019","journal-title":"NeuroImage"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1016\/j.clinph.2009.01.015","article-title":"Semi-automatic identification of independent components representing EEG artifact","volume":"120","author":"Viola","year":"2009","journal-title":"Clin. Neurophysiol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gabsteiger, F., Leutheuser, H., Reis, P., Lochmann, M., and Eskofier, B.M. SVM for Semi-automatic Selection of ICA Components of Electromyogenic Artifacts in EEG Data. Proceedings of the 15th International Conference on Biomedical Engineering, Singapore, 4\u20137 December 2013.","DOI":"10.1007\/978-3-319-02913-9_34"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/0013-4694(93)90110-H","article-title":"Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones","volume":"86","author":"Makeig","year":"1993","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","article-title":"EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis","volume":"134","author":"Delorme","year":"2004","journal-title":"J. Neurosci. Methods"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.1016\/j.neuroimage.2010.01.030","article-title":"EEG evidence of face-specific visual self-representation","volume":"50","author":"Miyakoshi","year":"2010","journal-title":"NeuroImage"},{"key":"ref_44","unstructured":"Nunez, P.L., and Srinivasan, R. (2007). Electric Fields of the Brain: The Neurophysics of EEG, Oxford University Press. [2nd ed.]."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.jneumeth.2014.08.002","article-title":"Digital filter design for electrophysiological data\u2014A practical approach","volume":"250","author":"Widmann","year":"2015","journal-title":"J. Neurosci. Methods"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Palmer, J.A., Makeig, S., Kreutz-Delgado, K., and Rao, B.D. (April, January 31). Newton method for the ICA mixture model. Proceedings of the 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, NV, USA.","DOI":"10.1109\/ICASSP.2008.4517982"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Delorme, A., Palmer, J., Onton, J., Oostenveld, R., and Makeig, S. (2012). Independent EEG Sources Are Dipolar. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0030135"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2011\/156869","article-title":"Fieldtrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data","volume":"2011","author":"Oostenveld","year":"2011","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"e17267","DOI":"10.7554\/eLife.17267","article-title":"Rotating waves during human sleep spindles organize global patterns of activity that repeat precisely through the night","volume":"5","author":"Muller","year":"2016","journal-title":"eLife"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.nicl.2014.09.006","article-title":"Cortical substrates and functional correlates of auditory deviance processing deficits in schizophrenia","volume":"6","author":"Rissling","year":"2014","journal-title":"NeuroImage Clin."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1098\/rstb.1999.0469","article-title":"Functionally independent components of early event-related potentials in a visual spatial attention task","volume":"354","author":"Makeig","year":"1999","journal-title":"Philos. Trans. R. Soc. B Biol. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.tics.2004.03.008","article-title":"Mining event-related brain dynamics","volume":"8","author":"Makeig","year":"2004","journal-title":"Trends Cogn. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1016\/j.neuroimage.2008.12.038","article-title":"Identifying reliable independent components via split-half comparisons","volume":"45","author":"Groppe","year":"2009","journal-title":"Neuroimage"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"036207","DOI":"10.1103\/PhysRevE.81.036207","article-title":"Detecting couplings between interacting oscillators with time-varying basic frequencies: Instantaneous wavelet bispectrum and information theoretic approach","volume":"81","author":"Stefanovska","year":"2010","journal-title":"Phys. Rev. E"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2018.06.001","article-title":"Surrogate data for hypothesis testing of physical systems","volume":"748","author":"Lancaster","year":"2018","journal-title":"Phys. Rep."},{"key":"ref_56","first-page":"33","article-title":"Neural Cross-Frequency Coupling Functions, Frontiers in Systems","volume":"11","author":"Stankovski","year":"2017","journal-title":"Neuroscience"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1007\/s002210100682","article-title":"Human EEG responses to 1\u2013100 Hz flicker: Resonance phenomena in visual cortex and their potential correlation to cognitive phenomena","volume":"137","author":"Herrmann","year":"2001","journal-title":"Exp. Brain Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/S1388-2457(00)00541-1","article-title":"The effects of ocular artifacts on (lateralized) broadband power in the EEG","volume":"112","author":"Hagemann","year":"2001","journal-title":"Clin. Neurophysiol."},{"key":"ref_59","unstructured":"Criswell, E. (2010). Cram\u2019s Introduction to Surface Electromyography, Jones and Bartlett Learning. [2nd ed.]."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1080\/00031305.1973.10478966","article-title":"Graphs in statistical analysis","volume":"27","author":"Anscombe","year":"1973","journal-title":"Am. Stat."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/24\/7040\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:42:38Z","timestamp":1760179358000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/24\/7040"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,9]]},"references-count":60,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["s20247040"],"URL":"https:\/\/doi.org\/10.3390\/s20247040","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,9]]}}}