Cross-sample entropy for the study of coordinated brain activity in calm and distress conditions with electroencephalographic recordings

García-Martínez, Beatriz; Fernández-Caballero, Antonio; Alcaraz, Raúl; Martínez-Rodrigo, Arturo

doi:10.1007/s00521-021-05694-4

Cited by 8 publications

(4 citation statements)

References 64 publications

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“…Interestingly, CSE has already demonstrated its ability to discern between calm and distress, revealing notable insights about the synchronization of the brain under these emotional states. 17 Hence, the present manuscript reinforces the capability of CSE to identify different emotional states from EEG signals.…”

Section: Discussionsupporting

confidence: 74%

“…16 The CSE index has already been computed by our research group for the recognition of two emotions, concretely calm and distress. 17 Nevertheless, to the best of our knowledge, this metric has never been used for the evaluation of more than two emotional states. In the scientific literature, the four groups of emotional states corresponding to the four quadrants of the circumplex model of emotions proposed by Russell have been typically analyzed with various methodologies.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Brain Functional Connectivity from Electroencephalographic Signals Under Different Emotional States

García-Martínez

Fernández-Caballero

Martínez-Rodrigo

et al. 2022

Int. J. Neur. Syst.

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The identification of the emotional states corresponding to the four quadrants of the valence/arousal space has been widely analyzed in the scientific literature by means of multiple techniques. Nevertheless, most of these methods were based on the assessment of each brain region separately, without considering the possible interactions among different areas. In order to study these interconnections, this study computes for the first time the functional connectivity metric called cross-sample entropy for the analysis of the brain synchronization in four groups of emotions from electroencephalographic signals. Outcomes reported a strong synchronization in the interconnections among central, parietal and occipital areas, while the interactions between left frontal and temporal structures with the rest of brain regions presented the lowest coordination. These differences were statistically significant for the four groups of emotions. All emotions were simultaneously classified with a 95.43% of accuracy, overcoming the results reported in previous studies. Moreover, the differences between high and low levels of valence and arousal, taking into account the state of the counterpart dimension, also provided notable findings about the degree of synchronization in the brain within different emotional conditions and the possible implications of these outcomes from a psychophysiological point of view.

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Section: Discussionsupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Evaluation of Brain Functional Connectivity from Electroencephalographic Signals Under Different Emotional States

García-Martínez

Fernández-Caballero

Martínez-Rodrigo

et al. 2022

Int. J. Neur. Syst.

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“…This measurement emerges as a natural candidate for the quantification of the signals' complexity [44,45]. In addition, these results were expressed in logarithmic form (E Log ) [45,46]. Finally, the last group referred to the time taken to complete the task.…”

Section: Feature Extractionmentioning

confidence: 99%

Physical Exercise Effects on University Students’ Attention: An EEG Analysis Approach

et al. 2022

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Physically active breaks (AB) are currently being proposed as an interesting tool to improve students’ attention. Reviews and meta-analyses confirm their effect on attention, but also warned about the sparse evidence based on vigilance and university students. Therefore, this pilot study aimed to (a) determine the effects of AB in comparison with passive breaks on university students’ vigilance and (b) to validate an analysis model based on machine learning algorithms in conjunction with a multiparametric model based on electroencephalography (EEG) signal features. Through a counterbalanced within-subject experimental study, six university students (two female; mean age = 25.67, STD = 3.61) had their vigilance performances (i.e., response time in Psycho-Motor Vigilance Task) and EEG measured, before and after a lecture with an AB and another lecture with a passive break. A multiparametric model based on the spectral power, signal entropy and response time has been developed. Furthermore, this model, together with different machine learning algorithms, shows that for the taken signals there are significant differences after the AB lesson, implying an improvement in attention. These differences are most noticeable with the SVM with RBF kernel and ANNs with F1-score of 85% and 88%, respectively. In conclusion, results showed that students performed better on vigilance after the lecture with AB. Although limited, the evidence found could help researchers to be more accurate in their EEG analyses and lecturers and teachers to improve their students’ attentions in a proper way.

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“…The results showed that the proposed solution increased the level of motivation to exercise and prevented muscle damage. In [15], a metric called cross-sample entropy was proposed to analyse EEG signal recordings. The results showed that the extracted parameters were useful to distinguish the states of calmness and distress in the subjects.…”

mentioning

confidence: 99%

Emotional State Measurement Trial (EMOPROEXE): A Protocol for Promoting Exercise in Adults and Children with Cerebral Palsy

Gómez-González,

Castro-García,

Merino-Monge

et al. 2024

JPM

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Background: The protocol described in this paper is part of a research project coordinated between three Spanish universities, where a technology aimed at improving the quality of life of people with cerebral palsy will be developed. Part of the proposed technology will consist of an interface and a series of applications to increase motivation for daily physical activity. The basis of these developments is the measurement of the emotional state of the subjects. Methods: The experimental protocol is designed with two research objectives, on the one hand to identify the emotional state through physiological signals, and on the other to determine whether music can be a motivating factor to promote physical activity. It is specifically designed for subjects with cerebral palsy, taking into account the special characteristics of this population. These are people with whom it is difficult to use questionnaires to have a basis to contrast with the measured physiological signals, so measurements must be taken in carefully chosen daily-life situations. Discussion: We hope our findings show which physiological parameters are the most robust to measure the emotional state and how to design rehabilitation and physical activity promotion routines that are motivating, in addition to being able to avoid risk factors during the performance of these routines. Trial registration: NCT05621057.

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Cross-sample entropy for the study of coordinated brain activity in calm and distress conditions with electroencephalographic recordings

Cited by 8 publications

References 64 publications

Evaluation of Brain Functional Connectivity from Electroencephalographic Signals Under Different Emotional States

Evaluation of Brain Functional Connectivity from Electroencephalographic Signals Under Different Emotional States

Physical Exercise Effects on University Students’ Attention: An EEG Analysis Approach

Emotional State Measurement Trial (EMOPROEXE): A Protocol for Promoting Exercise in Adults and Children with Cerebral Palsy

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