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Previous studies have suggested that infraslow brain activity could play an important role in cognition. Its scale-free properties (coarsely described by its 1/f power spectrum) are indeed modulated between contrasted conscious states (sleep vs. awake). However, finer modulations remain to be investigated. Here, we make use of a robust multifractal analysis to investigate the group-level impact of...
The analysis of scale-free (i.e., 1/f power spectrum) brain activity has emerged in the last decade since it has been shown that low frequency fluctuations interact with oscillatory activity in electrophysiology, noticeably when exogenous factors (stimuli, task) are delivered to the human brain. However, there are some major difficulties in measuring scale-free activity in neuroimaging data: they...
Fractal Analysis is the well developed theory in the Non-linear Analysis of Biomedical Signals such as Electroencephalogram (EEG). EEG signal is essentially multi scale fractal, i.e. Multifractal. Therefore Multifractal measures such as Generalized Fractal Dimensions (GFD), could be a useful tool to compute the degree of disorders, complexity, irregularity and chaotic nature of the Biomedical Signals...
Fractal Analysis is the well developed theory in the Non-linear Analysis of Biomedical Signals such as Electroencephalogram (EEG). EEG Biomedical signal is essentially multi scale fractal i.e., Multifractal. Therefore, quantifying the chaotic nature and complexity of the EEG Signal requires estimation of the Generalized Fractal Dimensions spectrum where the complexity means higher variability in general...
This article introduces a new feature vector extraction for EEG signals using multifractal analysis. The validity of the approach is asserted on real data sets from the BCI competitions II and III. The feature extraction can be performed in real time with low-cost discrete wavelet transforms. Classification results obtained with the new feature vectors are close to the state of art techniques, while...
This paper presents a method based on fractal dimensions to characterize electroencephalogram (EEG) signals, and differentiate between healthy and epileptic EEG data sets. The estimated correlation fractal dimension is considerably lower for intracranial invasive EEG recordings as compared to non-invasive scalp recordings. The epileptic EEG is also shown to have lower correlation dimension than healthy...
The Electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. It is highly subjective, and the symptoms may appear at random in the time scale. Traditional methods for nonlinear dynamic analysis, such as correlation dimension, Lyapunov exponent, approximate entropy, detrended fluctuation analysis, using a single parameter, cannot fully describe...
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