2019
DOI: 10.1016/j.measurement.2019.04.023
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Coherence-based correntropy spectral density: A novel coherence measure for functional connectivity of EEG signals

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Cited by 11 publications
(10 citation statements)
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“…Moreover, a recent application of correntropy to study classification scenarios of hands MI tasks showed that this measure was able to capture the expected spatial patterns of MI, while also performing better in terms of classification, when compared to other types of correlation metrics (namely, Pearson and Spearman correlations) [43]. Furthermore, even though other nonlinear approaches have been proposed to deal with these issues in the EEG signal, correntropy can be directly applied to the recorded data and, thus, generally becomes simpler and easier to compute [44]. We briefly describe the steps in the correntropy calculation in our study, below.…”
Section: Graphs Adjacency Matricesmentioning
confidence: 99%
See 1 more Smart Citation
“…Moreover, a recent application of correntropy to study classification scenarios of hands MI tasks showed that this measure was able to capture the expected spatial patterns of MI, while also performing better in terms of classification, when compared to other types of correlation metrics (namely, Pearson and Spearman correlations) [43]. Furthermore, even though other nonlinear approaches have been proposed to deal with these issues in the EEG signal, correntropy can be directly applied to the recorded data and, thus, generally becomes simpler and easier to compute [44]. We briefly describe the steps in the correntropy calculation in our study, below.…”
Section: Graphs Adjacency Matricesmentioning
confidence: 99%
“…We briefly describe the steps in the correntropy calculation in our study, below. A more through and mathematically formal discussion on correntropy can be found in [40,44]-the latter especifically concerning its application to EEG FC studies.…”
Section: Graphs Adjacency Matricesmentioning
confidence: 99%
“…Nevertheless, the selection of the connectivity measure is not entirely straightforward. Their dependence on an estimated cross-spectrum and subject data variability yield to low generalization capability in a wide range of scenarios [16,17].…”
Section: Introductionmentioning
confidence: 99%
“…The recent progress in this area has enabled devices to be controlled by brain signals [ 1 ]. The most used brain signals are electroencephalography (EEG) signals since they are non-invasive (measured from the scalp), have a high time resolution, and are relatively inexpensive [ 2 , 3 , 4 ]. Dealing with EEG signals is challenging because the signals are weak, may contain artefacts, are dependent on the patient’s mood and posture, and have low signal-to-noise ratio [ 5 ].…”
Section: Introductionmentioning
confidence: 99%