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Multivariate multiscale entropy (mvMSE) has been proposed as a combination of the coarse-graining process and multivariate sample entropy (mvSE) to quantify the irregularity of multivariate signals. However, both the coarse-graining process and mvSE may not be reliable for short signals. Although the coarse-graining process can be replaced with multivariate empirical mode decomposition (MEMD), the...
Using EEG signals to estimate cognitive state has drawn increasing attention in recently years, especially in the context of brain-computer interface (BCI) design. However, this goal is extremely difficult because, in addition to the complex relationships between the cognitive state and EEG signals that yields the non-stationarity of the features extracted from EEG signals, there are artefacts introduced...
The Scalp EEG is a large-scale & robust information source about neocortical dynamic functions. In this paper, we analyze a scalp Electro Encephalogram (EEG) database of 33 human subjects during the cognitive activity of Meditation, specifically Kriya Yoga. The information measures such as Renyi, Shannon entropies and Relative Energy of the different EEG Bands such as Alpha, Beta, & delta...
This study is aimed at characterizing three signal entropy measures, Approximate Entropy (ApEn), Sample Entropy (SampEn) and Multiscale Entropy (MSE) over real EEG signals when a number of samples are randomly lost due to, for example, wireless data transmission. The experimental EEG database comprises two main signal groups: control EEGs and epileptic EEGs. Results show that both SampEn and ApEn...
Synchronization analysis of EEG data has been so far performed by means of coherence functions or non-linear similarity quantifications. However, linear methods fail to provide information about the entire frequency spectrum or the direction of the interaction, while non-linear estimates require time-consuming computations, difficult parameter tuning and huge amounts of data. This paper, aims to overcome...
Transfer entropy (TE) is a recently proposed measure of the information flow between coupled linear or nonlinear systems. In this study, we suggest improvements in the selection of parameters for the estimation of TE that significantly enhance its accuracy and robustness in identifying the direction and the level of information flow between observed data series generated by coupled complex systems...
Permutation entropy (PE) is a new complexity measure which can extract important information from long, complex and high-dimensional time series. The advantages of this measure such as its fast calculation and robustness with respect to additive noise make it suitable for biomedical signal analysis. In this paper the ability of PE for characterizing the normal and epileptic EEG signals is investigated...
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