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Oct 31, 2019 · The results show that the use of the variance of multi-scale time series can extract more effective features compared with average indicators.
To address the problem of loss of time series information when taking the mean value of the average signal from EEG data, a variance index is used to ...
Feb 15, 2024 · On such basis, this study proposes a specific procedure based on Multiscale Fuzzy Entropy (MFE) for analyzing the complexity of the EEG traces ...
Missing: uniform | Show results with:uniform
The recently proposed multivariate multiscale entropy is capable of evaluating the complexity of a complex system of multi-channel data, but it is based on ...
Multiscale Entropy (MSE) is used to characterize the temporal irregularity of neural time series patterns. Due to its' presumed sensitivity to non-linear ...
Mar 20, 2022 · 2002) called modified multiscale entropy (mMSE) that directly quantifies the temporal irregularity of time-domain EEG/MEG/LFP signals at longer ...
Missing: uniform | Show results with:uniform
Jul 31, 2018 · Entropy can be viewed as a way to measure com-. plexity or information content of a given time series (sig-.
Feb 21, 2023 · Therefore, we use the RCmvMFE algorithm to analyze EEG signals from twenty subjects (eight control subjects and twelve MCI subjects). The ...
Jun 7, 2022 · Many studies have indicated that an entropy model can capture the dynamic characteristics of resting-state functional magnetic resonance ...
May 25, 2022 · The coarse-grained time series appears less irregular for larger scales due to averaging for uniform noise. ... Electroencephalogram-based ...