ArticleSensors (Basel, Switzerland)2019
Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
9 citing papers in PubMed, 44 citations in OpenAlex.
- KTU-MEDAFE: A Newly Developed Multimodal Dataset for Emotion Recognition Using EEG-Speech Decision-Level Fusion.Sensors (Basel, Switzerland) · 2026Article
- MSGM: a multi-scale spatiotemporal graph Mamba for EEG emotion recognition.Frontiers in neuroscience · 2026Article
- Towards predicting posttraumatic stress symptom severity using portable EEG-derived biomarkers.Scientific reports · 2025Article
- Towards predicting PTSD symptom severity using portable EEG-derived biomarkers.medRxiv : the preprint server for health sciences · 2024Article
- Effects of Robot-Assisted Activity Using a Communication Robot on Neurological Activity in Older Adults with and without Cognitive Decline.Journal of clinical medicine · 2023Article
- How the Brain Becomes the Mind: Can Thermodynamics Explain the Emergence and Nature of Emotions?Entropy (Basel, Switzerland) · 2022Article
- An integrated entropy-spatial framework for automatic gender recognition enhancement of emotion-based EEGs.Medical & biological engineering & computing · 2022Article
- Complexity and Entropy Analysis to Improve Gender Identification from Emotional-Based EEGs.Journal of healthcare engineering · 2021Article
- Psychological and Neurophysiological Effects of Robot Assisted Activity in Elderly People With Cognitive Decline.Gerontology & geriatric medicineArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
Abstract
Identifying emotions has become essential for comprehending varied human behavior during our daily lives. The electroencephalogram (EEG) has been adopted for eliciting information in terms of waveform distribution over the scalp. The rationale behind this work is twofold. First, it aims to propose spectral, entropy and temporal biomarkers for emotion identification. Second, it aims to integrate the spectral, entropy and temporal biomarkers as a means of developing spectro-spatial ( S S ) , entropy-spatial ( E S ) and temporo-spatial ( T S ) emotional profiles over the brain regions. The EEGs of 40 healthy volunteer students from the University of Vienna were recorded while they viewed seven brief emotional video clips. Features using spectral analysis, entropy method and temporal feature were computed. Three stages of two-way analysis of variance (ANOVA) were undertaken so as to identify the emotional biomarkers and Pearson's correlations were employed to determine the optimal explanatory profiles for emotional detection. The results evidence that the combination of applied spectral, entropy and temporal sets of features may provide and convey reliable biomarkers for identifying S S , E S and T S profiles relating to different emotional states over the brain areas. EEG biomarkers and profiles enable more comprehensive insights into various human behavior effects as an intervention on the brain.
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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.