Spatial permutation entropy distinguishes resting brain states

Boaretto, B. R. R.; Budzinski, Roberto C.; Rossi, K. L.; Masoller, Cristina; Macau, Elbert E. N.

doi:10.1016/j.chaos.2023.113453

Cited by 5 publications

(1 citation statement)

References 42 publications

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“…Even when the attenuation of the alpha waves over the human posterior cortex in the EO condition is a well-known recognized fact for almost a century [14], different approaches have more recently been proposed to obtain an improved discrimination and classification of these baseline brain states [2,7,[15][16][17][18][19]. It has been found, for example, that different entropy measures (permutation entropy, approximate entropy, multiscale entropy, spatial permutation entropy) achieve higher values in the EO condition compared with the EC one [2,16,18,20,21]. This can be explained by the increasing disorder and desynchronization of brain activity due to visual information receiving and processing [16].…”

Section: Introductionmentioning

confidence: 99%

Revisiting the Characterization of Resting Brain Dynamics with the Permutation Jensen–Shannon Distance

Zunino

2024

Entropy

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Taking into account the complexity of the human brain dynamics, the appropriate characterization of any brain state is a challenge not easily met. Actually, even the discrimination of simple behavioral tasks, such as resting with eyes closed or eyes open, represents an intricate problem and many efforts have been and are being made to overcome it. In this work, the aforementioned issue is carefully addressed by performing multiscale analyses of electroencephalogram records with the permutation Jensen–Shannon distance. The influence that linear and nonlinear temporal correlations have on the discrimination is unveiled. Results obtained lead to significant conclusions that help to achieve an improved distinction between these resting brain states.

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Section: Introductionmentioning

confidence: 99%

Revisiting the Characterization of Resting Brain Dynamics with the Permutation Jensen–Shannon Distance

Zunino

2024

Entropy

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Entropy of difference works similarly to permutation entropy for the assessment of anesthesia and sleep EEG despite the lower computational effort

Edthofer,

Ettel,

Schneider

et al. 2024

J Clin Monit Comput

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EEG monitoring during anesthesia or for diagnosing sleep disorders is a common standard. Different approaches for measuring the important information of this biosignal are used. The most often and efficient one for entropic parameters is permutation entropy as it can distinguish the vigilance states in the different settings. Due to high calculation times, it has mostly been used for low orders, although it shows good results even for higher orders. Entropy of difference has a similar way of extracting information from the EEG as permutation entropy. Both parameters and different algorithms for encoding the associated patterns in the signal are described. The runtimes of both entropic measures are compared, not only for the needed encoding but also for calculating the value itself. The mutual information that both parameters extract is measured with the AUC for a linear discriminant analysis classifier. Entropy of difference shows a smaller calculation time than permutation entropy. The reduction is much larger for higher orders, some of them can even only be computed with the entropy of difference. The distinguishing of the vigilance states between both measures is similar as the AUC values for the classification do not differ significantly. As the runtimes for the entropy of difference are smaller than for the permutation entropy, even though the performance stays the same, we state the entropy of difference could be a useful method for analyzing EEG data. Higher orders of entropic features may also be investigated better and more easily.

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Permutation entropy analysis of EEG signals for distinguishing eyes-open and eyes-closed brain states: Comparison of different approaches

Gancio,

Masoller,

Tirabassi

2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Developing reliable methodologies to decode brain state information from electroencephalogram (EEG) signals is an open challenge, crucial to implementing EEG-based brain–computer interfaces (BCIs). For example, signal processing methods that identify brain states could allow motor-impaired patients to communicate via non-invasive, EEG-based BCIs. In this work, we focus on the problem of distinguishing between the states of eyes closed (EC) and eyes open (EO), employing quantities based on permutation entropy (PE). An advantage of PE analysis is that it uses symbols (ordinal patterns) defined by the ordering of the data points (disregarding the actual values), hence providing robustness to noise and outliers due to motion artifacts. However, we show that for the analysis of multichannel EEG recordings, the performance of PE in discriminating the EO and EC states depends on the symbols’ definition and how their probabilities are estimated. Here, we study the performance of PE-based features for EC/EO state classification in a dataset of N=107 subjects with one-minute 64-channel EEG recordings in each state. We analyze features obtained from patterns encoding temporal or spatial information, and we compare different approaches to estimate their probabilities (by averaging over time, over channels, or by “pooling”). We find that some PE-based features provide about 75% classification accuracy, comparable to the performance of features extracted with other statistical analysis techniques. Our work highlights the limitations of PE methods in distinguishing the eyes’ state, but, at the same time, it points to the possibility that subject-specific training could overcome these limitations.

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Spatial permutation entropy distinguishes resting brain states

Cited by 5 publications

References 42 publications

Revisiting the Characterization of Resting Brain Dynamics with the Permutation Jensen–Shannon Distance

Revisiting the Characterization of Resting Brain Dynamics with the Permutation Jensen–Shannon Distance

Entropy of difference works similarly to permutation entropy for the assessment of anesthesia and sleep EEG despite the lower computational effort

Permutation entropy analysis of EEG signals for distinguishing eyes-open and eyes-closed brain states: Comparison of different approaches

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