ArticleSensors (Basel, Switzerland)2021
Predicting Exact Valence and Arousal Values from EEG.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Video-dominant emotion recognition for portable EEG-based devices.Scientific reports · 2026Article
- Nightingale: A multimodal approach combining EEG signals and audio features to enhance music therapy.PloS one · 2026Article
- A subject-specific mechanistic model of affect links electroencephalography, cardiorespiratory feedback, and monoaminergic dynamics.Frontiers in computational neuroscience · 2026Article
- Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.Biomimetics (Basel, Switzerland) · 2025Review
- Smart Seizure Detection System: Machine Learning Based Model in Healthcare IoT.Current aging science · 2025Article
- Developing assistive technology to support reminiscence therapy: a user-centered study.Frontiers in medicine · 2025Article
- Virtual reality-enabled high-performance emotion estimation with the most significant channel pairs.Heliyon · 2024Article
- EEG-based emotional valence and emotion regulation classification: a data-centric and explainable approach.Scientific reports · 2024Article
- Sneaky emotions: impact of data partitions in affective computing experiments with brain-computer interfacing.Biomedical engineering letters · 2024Article
- Classification of Emotional and Immersive Outcomes in the Context of Virtual Reality Scene Interactions.Diagnostics (Basel, Switzerland) · 2023Article
- Impact of negative links on the structural balance of brain functional network during emotion processing.Scientific reports · 2023Article
- Emotion Recognition Using Hierarchical Spatiotemporal Electroencephalogram Information from Local to Global Brain Regions.Bioengineering (Basel, Switzerland) · 2023Article
- Deep convolutional architecture-based hybrid learning for sleep arousal events detection through single-lead EEG signals.Brain and behavior · 2023Article
- A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges.Bioengineering (Basel, Switzerland) · 2023Review
- Assessment of Different Feature Extraction Methods for Discriminating Expressed Emotions during Music Performance towards BCMI Application.Sensors (Basel, Switzerland) · 2023Article
- Valence-arousal classification of emotion evoked by Chinese ancient-style music using 1D-CNN-BiLSTM model on EEG signals for college students.Multimedia tools and applications · 2023Article
- Embodied strategies for public speaking anxiety: evaluation of the Corp-Oral program.Frontiers in human neuroscience · 2023Article
- M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity.Sensors (Basel, Switzerland) · 2022Article
- LEDPatNet19: Automated Emotion Recognition Model based on Nonlinear LED Pattern Feature Extraction Function using EEG Signals.Cognitive neurodynamics · 2022Article
- Decoding the neural signatures of valence and arousal from portable EEG headset.Frontiers in human neuroscience · 2022Article
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Recognition of emotions from physiological signals, and in particular from electroencephalography (EEG), is a field within affective computing gaining increasing relevance. Although researchers have used these signals to recognize emotions, most of them only identify a limited set of emotional states (e.g., happiness, sadness, anger, etc.) and have not attempted to predict exact values for valence and arousal, which would provide a wider range of emotional states. This paper describes our proposed model for predicting the exact values of valence and arousal in a subject-independent scenario. To create it, we studied the best features, brain waves, and machine learning models that are currently in use for emotion classification. This systematic analysis revealed that the best prediction model uses a KNN regressor (K = 1) with Manhattan distance, features from the alpha, beta and gamma bands, and the differential asymmetry from the alpha band. Results, using the DEAP, AMIGOS and DREAMER datasets, show that our model can predict valence and arousal values with a low error (MAE < 0.06, RMSE < 0.16) and a strong correlation between predicted and expected values (PCC > 0.80), and can identify four emotional classes with an accuracy of 84.4%. The findings of this work show that the features, brain waves and machine learning models, typically used in emotion classification tasks, can be used in more challenging situations, such as the prediction of exact values for valence and arousal.
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