ArticleJournal of medical signals and sensors
Classification of Human Emotional States Based on Valence-Arousal Scale using Electroencephalogram.
Article in Journal of medical signals and sensors. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed, 12 citations in OpenAlex.
- Validating nonverbal cues for assessing physician empathy in telemedicine: a Delphi study.Medical education online · 2025Article
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
Recognition of human emotion states for affective computing based on Electroencephalogram (EEG) signal is an active yet challenging domain of research. In this study we propose an emotion recognition framework based on 2-dimensional valence-arousal model to classify High Arousal-Positive Valence (Happy) and Low Arousal-Negative Valence (Sad) emotions. In total 34 features from time, frequency, statistical and nonlinear domain are studied for their efficacy using Artificial Neural Network (ANN). The EEG signals from various electrodes in different scalp regions viz., frontal, parietal, temporal, occipital are studied for performance. It is found that ANN trained using features extracted from the frontal region has outperformed that of all other regions with an accuracy of 93.25%. The results indicate that the use of smaller set of electrodes for emotion recognition that can simplify the acquisition and processing of EEG data. The developed system can aid immensely to the physicians in their clinical practice involving emotional states, continuous monitoring, and development of wearable sensors for emotion recognition.
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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.