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Witte H, Wacker M. Time-frequency Techniques in Biomedical Signal Analysis. Methods Inf Med 2018; 52:279-96. [DOI: 10.3414/me12-01-0083] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2012] [Accepted: 11/16/2012] [Indexed: 11/09/2022]
Abstract
SummaryObjectives: This review outlines the method -ological fundamentals of the most frequently used non-parametric time-frequency analysis techniques in biomedicine and their main properties, as well as providing decision aids concerning their applications.Methods: The short-term Fourier transform (STFT), the Gabor transform (GT), the S-transform (ST), the continuous Morlet wavelet transform (CMWT), and the Hilbert transform (HT) are introduced as linear transforms by using a unified concept of the time-frequency representation which is based on a standardized analytic signal. The Wigner-Ville dis -tribution (WVD) serves as an example of the ‘quadratic transforms’ class. The combination of WVD and GT with the matching pursuit (MP) decomposition and that of the HT with the empirical mode decomposition (EMD) are explained; these belong to the class of signal-adaptive approaches.Results: Similarities between linear transforms are demonstrated and differences with regard to the time-frequency resolution and interference (cross) terms are presented in detail. By means of simulated signals the effects of different time-frequency resolutions of the GT, CMWT, and WVD as well as the resolution-related properties of the inter -ference (cross) terms are shown. The method-inherent drawbacks and their consequences for the application of the time-frequency techniques are demonstrated by instantaneous amplitude, frequency and phase measures and related time-frequency representations (spectrogram, scalogram, time-frequency distribution, phase-locking maps) of measured magnetoencephalographic (MEG) signals.Conclusions: The appropriate selection of a method and its parameter settings will ensure readability of the time-frequency representations and reliability of results. When the time-frequency characteristics of a signal strongly correspond with the time-frequency resolution of the analysis then a method may be considered ‘optimal’. The MP-based signal-adaptive approaches are preferred as these provide an appropriate time-frequency resolution for all frequencies while simultaneously reducing interference (cross) terms.
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Chella F, D'Andrea A, Basti A, Pizzella V, Marzetti L. Non-linear Analysis of Scalp EEG by Using Bispectra: The Effect of the Reference Choice. Front Neurosci 2017; 11:262. [PMID: 28559790 PMCID: PMC5432555 DOI: 10.3389/fnins.2017.00262] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Accepted: 04/24/2017] [Indexed: 11/13/2022] Open
Abstract
Bispectral analysis is a signal processing technique that makes it possible to capture the non-linear and non-Gaussian properties of the EEG signals. It has found various applications in EEG research and clinical practice, including the assessment of anesthetic depth, the identification of epileptic seizures, and more recently, the evaluation of non-linear cross-frequency brain functional connectivity. However, the validity and reliability of the indices drawn from bispectral analysis of EEG signals are potentially biased by the use of a non-neutral EEG reference. The present study aims at investigating the effects of the reference choice on the analysis of the non-linear features of EEG signals through bicoherence, as well as on the estimation of cross-frequency EEG connectivity through two different non-linear measures, i.e., the cross-bicoherence and the antisymmetric cross-bicoherence. To this end, four commonly used reference schemes were considered: the vertex electrode (Cz), the digitally linked mastoids, the average reference, and the Reference Electrode Standardization Technique (REST). The reference effects were assessed both in simulations and in a real EEG experiment. The simulations allowed to investigated: (i) the effects of the electrode density on the performance of the above references in the estimation of bispectral measures; and (ii) the effects of the head model accuracy in the performance of the REST. For real data, the EEG signals recorded from 10 subjects during eyes open resting state were examined, and the distortions induced by the reference choice in the patterns of alpha-beta bicoherence, cross-bicoherence, and antisymmetric cross-bicoherence were assessed. The results showed significant differences in the findings depending on the chosen reference, with the REST providing superior performance than all the other references in approximating the ideal neutral reference. In conclusion, this study highlights the importance of considering the effects of the reference choice in the interpretation and comparison of the results of bispectral analysis of scalp EEG.
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Affiliation(s)
- Federico Chella
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-PescaraChieti, Italy
- Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-PescaraChieti, Italy
| | - Antea D'Andrea
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-PescaraChieti, Italy
| | - Alessio Basti
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-PescaraChieti, Italy
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-PescaraChieti, Italy
- Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-PescaraChieti, Italy
| | - Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-PescaraChieti, Italy
- Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-PescaraChieti, Italy
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Chella F, Pizzella V, Zappasodi F, Nolte G, Marzetti L. Bispectral pairwise interacting source analysis for identifying systems of cross-frequency interacting brain sources from electroencephalographic or magnetoencephalographic signals. Phys Rev E 2016; 93:052420. [PMID: 27300936 DOI: 10.1103/physreve.93.052420] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Indexed: 11/07/2022]
Abstract
Brain cognitive functions arise through the coordinated activity of several brain regions, which actually form complex dynamical systems operating at multiple frequencies. These systems often consist of interacting subsystems, whose characterization is of importance for a complete understanding of the brain interaction processes. To address this issue, we present a technique, namely the bispectral pairwise interacting source analysis (biPISA), for analyzing systems of cross-frequency interacting brain sources when multichannel electroencephalographic (EEG) or magnetoencephalographic (MEG) data are available. Specifically, the biPISA makes it possible to identify one or many subsystems of cross-frequency interacting sources by decomposing the antisymmetric components of the cross-bispectra between EEG or MEG signals, based on the assumption that interactions are pairwise. Thanks to the properties of the antisymmetric components of the cross-bispectra, biPISA is also robust to spurious interactions arising from mixing artifacts, i.e., volume conduction or field spread, which always affect EEG or MEG functional connectivity estimates. This method is an extension of the pairwise interacting source analysis (PISA), which was originally introduced for investigating interactions at the same frequency, to the study of cross-frequency interactions. The effectiveness of this approach is demonstrated in simulations for up to three interacting source pairs and for real MEG recordings of spontaneous brain activity. Simulations show that the performances of biPISA in estimating the phase difference between the interacting sources are affected by the increasing level of noise rather than by the number of the interacting subsystems. The analysis of real MEG data reveals an interaction between two pairs of sources of central mu and beta rhythms, localizing in the proximity of the left and right central sulci.
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Affiliation(s)
- Federico Chella
- Department of Neuroscience, Imaging and Clinical Sciences, "Gabriele d'Annunzio" University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy.,Institute for Advanced Biomedical Technologies, "Gabriele d'Annunzio" University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, "Gabriele d'Annunzio" University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy.,Institute for Advanced Biomedical Technologies, "Gabriele d'Annunzio" University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy
| | - Filippo Zappasodi
- Department of Neuroscience, Imaging and Clinical Sciences, "Gabriele d'Annunzio" University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy.,Institute for Advanced Biomedical Technologies, "Gabriele d'Annunzio" University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy
| | - Guido Nolte
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, D-20246 Hamburg, Germany
| | - Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, "Gabriele d'Annunzio" University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy.,Institute for Advanced Biomedical Technologies, "Gabriele d'Annunzio" University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy
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Schiecke K, Wacker M, Benninger F, Feucht M, Leistritz L, Witte H. Matching Pursuit-Based Time-Variant Bispectral Analysis and its Application to Biomedical Signals. IEEE Trans Biomed Eng 2015; 62:1937-48. [DOI: 10.1109/tbme.2015.2407573] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Univariate normalization of bispectrum using Hölder's inequality. J Neurosci Methods 2014; 233:177-86. [PMID: 24975293 DOI: 10.1016/j.jneumeth.2014.05.030] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2014] [Revised: 05/22/2014] [Accepted: 05/24/2014] [Indexed: 11/21/2022]
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Chella F, Marzetti L, Pizzella V, Zappasodi F, Nolte G. Third order spectral analysis robust to mixing artifacts for mapping cross-frequency interactions in EEG/MEG. Neuroimage 2014; 91:146-61. [PMID: 24418509 DOI: 10.1016/j.neuroimage.2013.12.064] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2013] [Revised: 12/28/2013] [Accepted: 12/30/2013] [Indexed: 11/16/2022] Open
Abstract
We present a novel approach to the third order spectral analysis, commonly called bispectral analysis, of electroencephalographic (EEG) and magnetoencephalographic (MEG) data for studying cross-frequency functional brain connectivity. The main obstacle in estimating functional connectivity from EEG and MEG measurements lies in the signals being a largely unknown mixture of the activities of the underlying brain sources. This often constitutes a severe confounder and heavily affects the detection of brain source interactions. To overcome this problem, we previously developed metrics based on the properties of the imaginary part of coherency. Here, we generalize these properties from the linear to the nonlinear case. Specifically, we propose a metric based on an antisymmetric combination of cross-bispectra, which we demonstrate to be robust to mixing artifacts. Moreover, our metric provides complex-valued quantities that give the opportunity to study phase relationships between brain sources. The effectiveness of the method is first demonstrated on simulated EEG data. The proposed approach shows a reduced sensitivity to mixing artifacts when compared with a traditional bispectral metric. It also exhibits a better performance in extracting phase relationships between sources than the imaginary part of the cross-spectrum for delayed interactions. The method is then applied to real EEG data recorded during resting state. A cross-frequency interaction is observed between brain sources at 10Hz and 20Hz, i.e., for alpha and beta rhythms. This interaction is then projected from signal to source level by using a fit-based procedure. This approach highlights a 10-20Hz dominant interaction localized in an occipito-parieto-central network.
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Affiliation(s)
- F Chella
- Department of Neuroscience and Imaging, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University Foundation, Chieti, Italy.
| | - L Marzetti
- Department of Neuroscience and Imaging, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University Foundation, Chieti, Italy
| | - V Pizzella
- Department of Neuroscience and Imaging, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University Foundation, Chieti, Italy
| | - F Zappasodi
- Department of Neuroscience and Imaging, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University Foundation, Chieti, Italy
| | - G Nolte
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Wacker M, Putsche P, Witte H. Time-variant analysis of linear and non-linear phase couplings of and between frequency components of EEG burst patterns in full-term newborns. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2011; 2010:1706-9. [PMID: 21096402 DOI: 10.1109/iembs.2010.5626845] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Time-variant (tv) phase-locking and synchronization characteristics of and between low-frequency (≤ 1.5 Hz) and high-frequency EEG oscillations (≥ 3.5 Hz) of the tracé alternant (TA) pattern in full-term newborns have been quantified to explore the origin of quadratic phase coupling (QPC, as non-linear phase coupling measure) between the frequency ranges 1 - 1.5 Hz ⇔ 3.5 - 4.5 Hz, which characterize the specific interactions of oscillations during the TA's burst activity. Using the Gabor transformation two measures of linear phase coupling, the phase-locking index (PLI) and the n:m phase synchronization index (PSI) have been determined. Phase-locking within the frequency ranges 1 -1.5 Hz and 3.5 - 4.5 Hz, and synchronization between both frequency ranges exists. These phase characteristics are significant 2 sec after burst onset and are associated with the maximum-values of the QPC(1 - 1.5 Hz ⇔ 3.5 - 4.5 Hz) which demonstrates that a specific neuronal coordination between the dynamics of phases and of amplitude-frequency dependencies must be underlying.
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Affiliation(s)
- Matthias Wacker
- Institute for Medical Statistics, Computer Sciences and Documentation, Bernstein Group for Computational Neuroscience Jena, Friedrich Schiller University Jena, Germany.
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Witte H, Putsche P, Eiselt M, Schwab K, Wacker M, Leistritz L. Time-variant analysis of phase couplings and amplitude–frequency dependencies of and between frequency components of EEG burst patterns in full-term newborns. Clin Neurophysiol 2011; 122:253-66. [DOI: 10.1016/j.clinph.2010.07.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2009] [Revised: 06/14/2010] [Accepted: 07/02/2010] [Indexed: 10/19/2022]
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Coordination of the EEG and the heart rate of preterm neonates during quiet sleep. Neurosci Lett 2009; 465:252-6. [PMID: 19766578 DOI: 10.1016/j.neulet.2009.09.031] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2009] [Revised: 09/14/2009] [Accepted: 09/14/2009] [Indexed: 11/23/2022]
Abstract
Aim of this study was to confirm that EEG bursts are associated with heart rate (HR) accelerations, and to investigate the synchronicity between quadratic phase couplings (QPC) courses of the EEG and HR before and during burst activity during quiet sleep in preterm newborns. The time-courses of QPC between frequency components of the EEG ([0.25-1.0 Hz]<-->[4.0-6.0 Hz]) as well as between the Mayer-Traube-Hering (MTH) wave and the frequency component of the HR associated to the respiratory sinus arrhythmia (RSA) ([0.02-0.15 Hz]<-->[0.4-1.5 Hz]) were investigated in five preterm neonates. During quiet sleep, the EEG alternates between burst and interburst activity. The burst onsets were used to trigger an averaging procedure for the EEG, HR, and QPC courses. It can be demonstrated that the envelopes of the EEG rise after the burst onset accompanied by an acceleration of HR before or at the burst maximum. The QPC courses show that the HR's QPC increases before or at the burst onset whereas the increase of the EEG's QPC is delayed. The synchronous changes of EEG and HR as well as of the corresponding QPC courses indicate a coupling between cortical, thalamocortical and neurovegetative brain structures. Such a coupling might be mediated by the MTH waves in the blood pressure.
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Canonical bicoherence analysis of dynamic EEG data. J Comput Neurosci 2009; 29:23-34. [DOI: 10.1007/s10827-009-0177-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2008] [Revised: 06/01/2009] [Accepted: 07/03/2009] [Indexed: 11/25/2022]
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