Analysis of the interrelations between frequency bands of the EEG by means of the bispectrum a preliminary studyAnalyse des interrelations entre des differentes bandes de frequence de l'eeg par la methode de l'analyse bispectrale (etude preliminaire)☆,☆☆
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Cited by (100)
Benchmarking HEp-2 specimen cells classification using linear discriminant analysis on higher order spectra features of cell shape
2019, Pattern Recognition LettersCitation Excerpt :More information about the development of higher order statistics and spectra from first principles and the third order spectrum, which is known as “bispectrum”, can be found in [9,10]. HOS have been used and applied to signal processing applications since the1970s, for example, EEG analysis [16], modeling ultrasound radio-frequency (RF) echo [1], and extraction of features from lung sounds [24]. Pattern recognition and medical imaging applications include: one dimensional pattern recognition [13], two dimensional images and object recognition [41], speaker identification [14], subsurface interface detection with ground penetrating radar [39], viruses classification in electron microscope images [30], sea mines classification in sonar images [12], eye abnormalities detection from digital fundus images [40] and periocular biometric recognition images [6].
Surrogate data for hypothesis testing of physical systems
2018, Physics ReportsCitation Excerpt :Unlike synchronization methods, which are unable to provide any information about the characteristics of couplings, bispectrum analysis is especially powerful for the investigation of the nature of interactions between coupled oscillators, while making no assumptions about the data under investigation. First introduced by Hasselmann [193] to study ocean waves, bispectral analysis has since been applied in oceanography [194], and has also gained popularity in biomedical sciences, for example in the analysis of EEG [195–200], cardio-respiratory interactions [201] and abnormalities in respiration [202]. Bispectrum analysis has also proven useful in geophysics [203], turbulence analysis [204–206], astrophysics [191], plasma physics [207], machine engineering [208], economics [209] and seismology [210].
The bispectrum and its relationship to phase-amplitude coupling
2018, NeuroImageEstimation of nonlinear neural source interactions via sliced bicoherence
2016, Biomedical Signal Processing and ControlCitation Excerpt :Examples for the latter utilize the simple parametric formulae assuming standard distributions (Gaussian, uniform etc.) for the assessment of coherence [10] and phase-amplitude coupling [11] confidence limits. There have been various studies using bispectrum and its normalized version named as bicoherence on EEG and MEG data [12–16]. We would like to emphasize the expensive computational cost as one of the main obstacles over the bispectral measures.
Univariate normalization of bispectrum using Hölder's inequality
2014, Journal of Neuroscience MethodsThird order spectral analysis robust to mixing artifacts for mapping cross-frequency interactions in EEG/MEG
2014, NeuroImageCitation Excerpt :In this paper, we address the problem of mixing artifacts in relation to the use of nonlinear methods for studying cross-frequency phase-synchronization between neuronal populations. Specifically we refer to bispectral measures, which were developed and applied on EEG/MEG in abundance (Darvas et al., 2009a, 2009b; Dumermuth et al., 1971; Helbig et al., 2006; Jirsa and Müller, 2013; Schwilden, 2006; Wang et al., 2007), and we examine the question of what information can be derived from such measures that estimate true functional connectivity between brain regions as opposed to mixing artifacts. Our new contribution is, essentially, the generalization to nonlinear methods of the concepts based on the imaginary part of coherency to solve the problem of volume conduction (Marzetti et al., 2008; Nolte et al., 2004, 2008, 2009).
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Paper presented in part as a free communication at the VIIth International Congress of EEG and Clinical Neurophysiology, San Diego, U.S.A., September, 1969 (Kleiner et al. 1969).
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Supported in part by the “Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung”, The “Emil Barell Stiftung der F. Hoffmann-La Roche zur Förderung der Medizinisch-Wissenschaftlichen Forschung”, and the “Fritz Hoffmann-La Roche-Stiftung zur Förderung Wissenschaftlicher Arbeitsgemeinshaften in der Schweiz”.
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The authors thank A. Schai and H. Ammann from the Computer Centre of the Swiss Federal Institute of Technology in Zürich for their help. The assistance of O. Brunner, photographer at the Children's Hospital Zürich is gratefully acknowledged.
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P.J. Huber and Th. Gasser are now with the Dept. of Mathematical Statistics, Princeton University, Princeton, U.S.A.