ReviewBrain informatics2021
EEG-based human emotion recognition using entropy as a feature extraction measure.
Review in Brain informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 108 citations in OpenAlex.
- The impact of the number of regions of interest and their configurations on EEG brain-connectivity in emotion recognition.Cognitive processing · 2026Article
- Evaluating multi-level membership inference risk in federated EEG learning.Brain informatics · 2026Article
- Bidirectional coupling of physiology and emotions reveals unique speaker-listener dynamics.Social cognitive and affective neuroscience · 2026Article
- Development of a Measurement Procedure for Emotional States Detection Based on Single-Channel Ear-EEG: A Proof-of-Concept Study.Sensors (Basel, Switzerland) · 2026Article
- Continuous bidirectional coupling of heart rate variability and emotions in dyad speaker-listener dynamics reveals unique interpersonal synchronization.Frontiers in systems neuroscience · 2026Article
- EEG emotion recognition via multilevel entropy analysis: The power of entropy of entropy on phase dynamics.PloS one · 2026Article
- Entropy as a Tool for Quantifying Biomedical Signals: Gaps and Opportunities in Clinical Methodological Approaches-A Scoping Review.Biomedical engineering and computational biology · 2026Review
- Traditional Machine Learning Outperforms EEGNet for Consumer-Grade EEG Emotion Recognition: A Comprehensive Evaluation with Cross-Dataset Validation.Sensors (Basel, Switzerland) · 2025Article
- From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications.Brain sciences · 2025Review
- Towards predicting posttraumatic stress symptom severity using portable EEG-derived biomarkers.Scientific reports · 2025Article
- A Resource-Efficient Multi-Entropy Fusion Method and Its Application for EEG-Based Emotion Recognition.Entropy (Basel, Switzerland) · 2025Article
- Exploring physical and functional EEG connectivity with multilayer graph transformer convolutional networks for emotion recognition.Frontiers in human neuroscience · 2025Article
- Applications of Entropy in Data Analysis and Machine Learning: A Review.Entropy (Basel, Switzerland) · 2024Review
- EEG emotion recognition based on an innovative information potential index.Cognitive neurodynamics · 2024Article
- Decoded EEG neurofeedback-guided cognitive reappraisal training for emotion regulation.Cognitive neurodynamics · 2024Article
- Article
- Towards predicting PTSD symptom severity using portable EEG-derived biomarkers.medRxiv : the preprint server for health sciences · 2024Article
- Investigating Cortical Complexity in Mixed Dementia through Nonlinear Dynamic Analyses: A Resting-State EEG Study.Iranian journal of psychiatry · 2024Article
- Lemniscate of Bernoulli's map quantifiers: innovative measures for EEG emotion recognition.Cognitive neurodynamics · 2024Article
- Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many studies on brain-computer interface (BCI) have sought to understand the emotional state of the user to provide a reliable link between humans and machines. Advanced neuroimaging methods like electroencephalography (EEG) have enabled us to replicate and understand a wide range of human emotions more precisely. This physiological signal, i.e., EEG-based method is in stark comparison to traditional non-physiological signal-based methods and has been shown to perform better. EEG closely measures the electrical activities of the brain (a nonlinear system) and hence entropy proves to be an efficient feature in extracting meaningful information from raw brain waves. This review aims to give a brief summary of various entropy-based methods used for emotion classification hence providing insights into EEG-based emotion recognition. This study also reviews the current and future trends and discusses how emotion identification using entropy as a measure to extract features, can accomplish enhanced identification when using EEG signal.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.