Preben Kidmose

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A framework for the robust assessment of phase synchrony between multichannel observations is introduced. This is achieved by using Empirical Mode Decomposition (EMD), a data driven technique which decomposes nonlinear and nonstationary data into their oscillatory components (scales). In general, it is rarely possible to jointly process two or more channels(More)
The integration of brain monitoring based on electroencephalography (EEG) into everyday life has been hindered by the limited portability and long setup time of current wearable systems as well as by the invasiveness of implanted systems (e.g. intracranial EEG). We explore the potential to record EEG in the ear canal, leading to a discreet, unobtrusive, and(More)
OBJECTIVE The initiation of treatment for women with threatening preterm labor requires effective distinction between true and false labor. The electrohysterogram (EHG) has shown great promise in estimating and classifying uterine activity. However, key issues remain unresolved and no clinically usable method has yet been presented using EHG. Recent studies(More)
A data-adaptive algorithm for the entropy-based analysis of structural regularities (complexity) in multivariate signals is proposed. This is achieved by combining multivariate sample entropy with a multivariate extension of empirical mode decomposition, both data-driven multiscale techniques. The proposed analysis across data-adaptive scales makes the(More)
A novel method is introduced to determine asymmetry, the lateralization of brain activity, using extension of the algorithm empirical mode decomposition (EMD). The localized and adaptive nature of EMD make it highly suitable for estimating amplitude information across frequency for nonlinear and nonstationary data. Analysis illustrates how bivariate(More)
A method for brain monitoring based on measuring the electroencephalogram (EEG) from electrodes placed in-the-ear (ear-EEG) was recently proposed. The objective of this study is to further characterize the ear-EEG and perform a rigorous comparison against conventional on-scalp EEG. This is achieved for both auditory and visual evoked responses, over(More)
Highlights Auditory middle and late latency responses can be recorded reliably from ear-EEG.For sources close to the ear, ear-EEG has the same signal-to-noise-ratio as scalp.Ear-EEG is an excellent match for power spectrum-based analysis. A method for measuring electroencephalograms (EEG) from the outer ear, so-called ear-EEG, has recently been proposed.(More)
A method for brain monitoring based on measuring electroencephalographic (EEG) signals from electrodes placed in-the-ear (Ear-EEG) was recently proposed. The Ear-EEG recording methodology provides a non-invasive, discreet and unobtrusive way of measuring electrical brain signals and has great potential as an enabling method for brain monitoring in everyday(More)
ALPHA-STABLE DISTRIBUTIONS Preben Kidmose Informatics and Mathematical Modelling, Richard Petersens Plads, Building 321, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark. ABSTRACT A method for identifying the independent components of an alpha-stable random vector is proposed. The method is based on an estimate of the spectral measure for(More)
We introduce a novel approach to brain monitoring based on electroencephalogram (EEG) recordings from within the ear canal. While existing clinical and wearable systems are limited in terms of portability and ease of use, the proposed in-the-ear (ITE) recording platform promises a number of advantages including ease of implementation, minimally intrusive(More)