{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T17:13:29Z","timestamp":1793294009402,"version":"4.5.0"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T00:00:00Z","timestamp":1639008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2017R1A6A1A03015496"],"award-info":[{"award-number":["2017R1A6A1A03015496"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:p>Studies on brain mechanisms enable us to treat various brain diseases and develop diverse technologies for daily life. Therefore, an analysis method of neural signals is critical, as it provides the basis for many brain studies. In many cases, researchers want to understand how neural signals change according to different conditions. However, it is challenging to find distinguishing characteristics, and doing so requires complex statistical analysis. In this study, we propose a novel analysis method, FTF (F-value time-frequency) analysis, that applies the F-value of ANOVA to time-frequency analysis. The proposed method shows the statistical differences among conditions in time and frequency. To evaluate the proposed method, electroencephalography (EEG) signals were analyzed using the proposed FTF method. The EEG signals were measured during imagined movement of the left hand, right hand, foot, and tongue. The analysis revealed the important characteristics which were different among different conditions and similar within the same condition. The FTF analysis method will be useful in various fields, as it allows researchers to analyze how frequency characteristics vary according to different conditions.<\/jats:p>","DOI":"10.3389\/fnins.2021.729449","type":"journal-article","created":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T16:36:24Z","timestamp":1639067784000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["F-Value Time-Frequency Analysis: Between-Within Variance Analysis"],"prefix":"10.3389","volume":"15","author":[{"given":"Hong Gi","family":"Yeom","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyundoo","family":"Jeong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,12,9]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/MEMB.2009.934244","article-title":"Time-frequency analysis of biosignals a wavelet transform overview.","volume":"28","author":"Addison","year":"2009","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"B2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.phycom.2009.07.001","article-title":"Emerging applications of wavelets: a review.","volume":"3","author":"Akansu","year":"2010","journal-title":"Phys. Commun."},{"key":"B3","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1177\/1087054712460087","article-title":"A decade of EEG Theta\/Beta ratio research in ADHD: a meta-analysis.","volume":"17","author":"Arns","year":"2013","journal-title":"J. Attent. Disord."},{"key":"B4","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.simpat.2017.08.004","article-title":"Wavelet ANOVA approach to model validation.","volume":"78","author":"Atkinson","year":"2017","journal-title":"Simul. Model. Pract. Theory"},{"key":"B5","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.simpat.2017.10.002","article-title":"Wavelet ANOVA bisection method for identifying simulation model bias.","volume":"80","author":"Atkinson","year":"2018","journal-title":"Simul. Model. Pract. Theory"},{"key":"B6","doi-asserted-by":"crossref","DOI":"10.1201\/9781351073127","author":"Beyer","year":"2019","journal-title":"Handbook of Tables for Probability and Statistics."},{"key":"B7","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.neuroimage.2007.01.051","article-title":"The non-invasive Berlin brain-computer interface: fast acquisition of effective performance in untrained subjects.","volume":"37","author":"Blankertz","year":"2007","journal-title":"Neuroimage"},{"key":"B8","doi-asserted-by":"publisher","first-page":"2452","DOI":"10.1109\/TBME.2008.923152","article-title":"The Berlin brain-computer interface: accurate performance from first-session in BCI-naive subjects.","volume":"55","author":"Blankertz","year":"2008","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"B9","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.schres.2007.11.020","article-title":"The status of spectral EEG abnormality as a diagnostic test for schizophrenia.","volume":"99","author":"Boutros","year":"2008","journal-title":"Schizophr. Res."},{"key":"B10","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.neuroimage.2019.05.048","article-title":"A better way to define and describe Morlet wavelets for time-frequency analysis.","volume":"199","author":"Cohen","year":"2019","journal-title":"Neuroimage"},{"key":"B11","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1016\/S0140-6736(12)61816-9","article-title":"High-performance neuroprosthetic control by an individual with tetraplegia.","volume":"381","author":"Collinger","year":"2013","journal-title":"Lancet"},{"key":"B12","doi-asserted-by":"publisher","first-page":"487","DOI":"10.2174\/156720510792231720","article-title":"Diagnosis of Alzheimer\u2019s disease from EEG signals: where are we standing?","volume":"7","author":"Dauwels","year":"","journal-title":"Curr. Alzheimer Res."},{"key":"B13","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1016\/j.neuroimage.2009.06.056","article-title":"A comparative study of synchrony measures for the early diagnosis of Alzheimer\u2019s disease based on EEG.","volume":"49","author":"Dauwels","year":"","journal-title":"Neuroimage"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dcn.2015.08.004","article-title":"An EEG study on the somatotopic organisation of sensorimotor cortex activation during action execution and observation in infancy.","volume":"15","author":"de Klerk","year":"2015","journal-title":"Dev. Cogn. Neurosci."},{"key":"B15","doi-asserted-by":"publisher","first-page":"251","DOI":"10.3389\/fnins.2020.00251","article-title":"Deep learning convolutional neural networks discriminate adult ADHD from healthy individuals on the basis of event-related spectral EEG.","volume":"14","author":"Dubreuil-Vall","year":"2020","journal-title":"Front. Neurosci."},{"key":"B16","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.cmpb.2011.11.005","article-title":"Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier.","volume":"108","author":"Fraiwan","year":"2012","journal-title":"Comput. Methods Prog. Biomed."},{"key":"B17","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4612-0003-1","author":"Grochenig","year":"2001","journal-title":"Foundations of Time-Frequency Analysis: With 15 Figures."},{"key":"B18","doi-asserted-by":"publisher","first-page":"685119","DOI":"10.3389\/fnins.2021.685119","article-title":"A novel smart motor imagery intention human-computer interaction model using extreme learning machine and EEG signals.","volume":"15","author":"Gu","year":"2021","journal-title":"Front. Neurosci."},{"key":"B19","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1007\/s10548-013-0327-5","article-title":"Time-frequency analysis of event-related potentials: a brief tutorial.","volume":"27","author":"Herrmann","year":"2014","journal-title":"Brain Topogr."},{"key":"B20","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1038\/nature11076","article-title":"Reach and grasp by people with tetraplegia using a neurally controlled robotic arm.","volume":"485","author":"Hochberg","year":"2012","journal-title":"Nature"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1016\/j.clinph.2015.07.015","article-title":"Usefulness of multiple frequency band source localizations in ictal MEG.","volume":"127","author":"Jeong","year":"2016","journal-title":"Clin. Neurophysiol."},{"key":"B22","doi-asserted-by":"crossref","first-page":"659","DOI":"10.3171\/2019.10.JNS191932","article-title":"Critical area for memory decline after mesial temporal resection in epilepsy patients","volume":"134","author":"Kim","year":"2021","journal-title":"J. Neurosurg."},{"key":"B23","doi-asserted-by":"publisher","first-page":"026002","DOI":"10.1088\/1741-2552\/ab7490","article-title":"Using machine learning to reveal the population vector from EEG signals.","volume":"17","author":"Kobler","year":"2020","journal-title":"J. Neural Eng."},{"key":"B24","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1007\/s11920-014-0498-0","article-title":"Use of EEG to diagnose ADHD.","volume":"16","author":"Lenartowicz","year":"2014","journal-title":"Curr. Psychiatry Rep."},{"key":"B25","doi-asserted-by":"publisher","first-page":"1232","DOI":"10.1016\/j.clinph.2008.01.104","article-title":"Abnormal EEG complexity in patients with schizophrenia and depression.","volume":"119","author":"Li","year":"2008","journal-title":"Clin. Neurophysiol."},{"key":"B26","doi-asserted-by":"publisher","first-page":"687053","DOI":"10.3389\/fnins.2021.687053","article-title":"Differences in multimodal electroencephalogram and clinical correlations between early-onset Alzheimer\u2019s disease and frontotemporal dementia.","volume":"15","author":"Lin","year":"2021","journal-title":"Front. Neurosci."},{"key":"B27","doi-asserted-by":"publisher","first-page":"102782","DOI":"10.1016\/j.marstruc.2020.102782","article-title":"A new time-frequency analysis method based on single mode function decomposition for offshore wind turbines.","volume":"72","author":"Liu","year":"2020","journal-title":"Mar. Struct."},{"key":"B28","doi-asserted-by":"publisher","first-page":"046031","DOI":"10.1088\/1741-2552\/aba6f7","article-title":"Continuous low-frequency EEG decoding of arm movement for closed-loop, natural control of a robotic arm.","volume":"17","author":"Mondini","year":"2020","journal-title":"J. Neural Eng."},{"key":"B29","doi-asserted-by":"publisher","first-page":"642251","DOI":"10.3389\/fnins.2021.642251","article-title":"Decoding covert speech from EEG-a comprehensive review.","volume":"15","author":"Panachakel","year":"2021","journal-title":"Front. Neurosci."},{"key":"B30","doi-asserted-by":"publisher","first-page":"1842","DOI":"10.1016\/s1388-2457(99)00141-8","article-title":"Event-related EEG\/MEG synchronization and desynchronization: basic principles.","volume":"110","author":"Pfurtscheller","year":"1999","journal-title":"Clin. Neurophysiol."},{"key":"B31","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1093\/schbul\/sbn093","article-title":"Event-related EEG time-frequency analysis: an overview of measures and an analysis of early gamma band phase locking in schizophrenia.","volume":"34","author":"Roach","year":"2008","journal-title":"Schizophr. Bull."},{"key":"B32","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","article-title":"Complex network measures of brain connectivity: uses and interpretations.","volume":"52","author":"Rubinov","year":"2010","journal-title":"Neuroimage"},{"key":"B33","author":"Scheff\u00e9","year":"1999","journal-title":"The Analysis of Variance."},{"key":"B34","doi-asserted-by":"publisher","first-page":"582","DOI":"10.3389\/fnins.2020.00582","article-title":"EEG biomarkers related with the functional state of stroke patients.","volume":"14","author":"Sebastian-Romagosa","year":"2020","journal-title":"Front. Neurosci."},{"key":"B35","doi-asserted-by":"publisher","first-page":"2070","DOI":"10.1016\/j.clinph.2017.06.254","article-title":"The standardized EEG electrode array of the IFCN.","volume":"128","author":"Seeck","year":"2017","journal-title":"Clin. Neurophysiol."},{"key":"B36","doi-asserted-by":"publisher","first-page":"016065","DOI":"10.1088\/1741-2552\/ab59a7","article-title":"Reconstruction of hand, elbow and shoulder actual and imagined trajectories in 3D space using EEG slow cortical potentials.","volume":"17","author":"Sosnik","year":"2020","journal-title":"J. Neural Eng."},{"key":"B37","doi-asserted-by":"publisher","first-page":"55","DOI":"10.3389\/fnins.2012.00055","article-title":"Review of the BCI competition IV.","volume":"6","author":"Tangermann","year":"2012","journal-title":"Front. Neurosci."},{"key":"B38","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1109\/TITB.2009.2017939","article-title":"Epileptic seizure detection in EEGs using time-frequency analysis.","volume":"13","author":"Tzallas","year":"2009","journal-title":"IEEE Trans. Inform. Technol. Biomed."},{"key":"B39","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1007\/s13538-020-00842-y","article-title":"A tutorial review on time-frequency analysis of non-stationary vibration signals with nonlinear dynamics applications.","volume":"51","author":"Varanis","year":"2021","journal-title":"Braz. J. Phys."},{"key":"B40","doi-asserted-by":"crossref","first-page":"279","DOI":"10.3414\/ME12-01-0083","article-title":"Time-frequency techniques in biomedical signal analysis a tutorial review of similarities and differences.","volume":"52","author":"Wacker","year":"2013","journal-title":"Methods Inform. Med."},{"key":"B41","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1017\/cjn.2018.373","article-title":"Magnetoencephalographic source localization of the eye area of the motor homunculus.","volume":"46","author":"Wennberg","year":"2019","journal-title":"Can. J. Neurol. Sci."},{"key":"B42","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.ymssp.2018.07.039","article-title":"Parameterised time-frequency analysis methods and their engineering applications: a review of recent advances.","volume":"119","author":"Yang","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"B43","doi-asserted-by":"publisher","first-page":"026006","DOI":"10.1088\/1741-2560\/10\/2\/026006","article-title":"Estimation of the velocity and trajectory of three-dimensional reaching movements from non-invasive magnetoencephalography signals.","volume":"10","author":"Yeom","year":"2013","journal-title":"J. Neural Eng."},{"key":"B44","doi-asserted-by":"publisher","first-page":"e0103539","DOI":"10.1371\/journal.pone.0103539","article-title":"High-accuracy brain-machine interfaces using feedback information.","volume":"9","author":"Yeom","year":"2014","journal-title":"PLoS One"},{"key":"B45","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1038\/s41598-020-57489-7","article-title":"Brain mechanisms in motor control during reaching movements: transition of functional connectivity according to movement states.","volume":"10","author":"Yeom","year":"","journal-title":"Sci. Rep."},{"key":"B46","doi-asserted-by":"crossref","first-page":"20146","DOI":"10.1109\/ACCESS.2020.2969720","article-title":"LSTM improves accuracy of reaching trajectory prediction from magnetoencephalography signals.","volume":"8","author":"Yeom","year":"","journal-title":"IEEE Access"}],"container-title":["Frontiers in Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnins.2021.729449\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T16:36:37Z","timestamp":1639067797000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnins.2021.729449\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,9]]},"references-count":46,"alternative-id":["10.3389\/fnins.2021.729449"],"URL":"https:\/\/doi.org\/10.3389\/fnins.2021.729449","relation":{},"ISSN":["1662-453X"],"issn-type":[{"value":"1662-453X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,9]]},"article-number":"729449"}}