ArticleFrontiers in neuroinformatics2019
Multi-Lag Analysis of Symbolic Entropies on EEG Recordings for Distress Recognition.
Article in Frontiers in neuroinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Quantifying attention in children with intellectual and developmental disabilities through multicenter electrooculogram signal analysis.Scientific reports · 2024Article
- Cross subject emotion identification from multichannel EEG sub-bands using Tsallis entropy feature and KNN classifier.Brain informatics · 2024Article
- Stress Response Analysis via Dynamic Entropy in EEG: Caregivers in View.International journal of environmental research and public health · 2023Article
- Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations.The European journal of neuroscience · 2022Review
- CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals.Entropy (Basel, Switzerland) · 2021Article
- Conditional Entropy: A Potential Digital Marker for Stress.Entropy (Basel, Switzerland) · 2021Article
- Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers.Sensors (Basel, Switzerland) · 2019Article
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Authors and funding
5 authors.
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Abstract
Distress is a critical problem in developed societies given its long-term negative effects on physical and mental health. The interest in studying this emotion has notably increased during last years, being electroencephalography (EEG) signals preferred over other physiological variables in this research field. In addition, the non-stationary nature of brain dynamics has impulsed the use of non-linear metrics, such as symbolic entropies in brain signal analysis. Thus, the influence of time-lag on brain patterns assessment has not been tested. Hence, in the present study two permutation entropies denominated Delayed Permutation Entropy and Permutation Min-Entropy have been computed for the first time at different time-lags to discern between emotional states of calmness and distress from EEG signals. Moreover, a number of curve-related features were also calculated to assess brain dynamics across different temporal intervals. Complementary information among these variables was studied through sequential forward selection and 10-fold cross-validation approaches. According to the results obtained, the multi-lag entropy analysis has been able to reveal new significant insights so far undiscovered, thus notably improving the process of distress recognition from EEG recordings.
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