ArticleFrontiers in neuroscience2021
Cross-Subject EEG-Based Emotion Recognition Through Neural Networks With Stratified Normalization.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- On the prediction models for brain signal-based emotion recognition.Scientific reports · 2026Article
- Depression Detection from Three-Channel Resting-State EEG Using a Hybrid Conv1D and Spectral-Statistical Fusion Model.Sensors (Basel, Switzerland) · 2026Article
- Complex emotion recognition system using basic emotions via facial expression, electroencephalogram, and electrocardiogram signals: a review.Frontiers in psychology · 2026Review
- Review
- Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.Sensors (Basel, Switzerland) · 2024Article
- The applied principles of EEG analysis methods in neuroscience and clinical neurology.Military Medical Research · 2023Review
- Mini review: Challenges in EEG emotion recognition.Frontiers in psychology · 2023Review
- Not All Electrode Channels Are Needed: Knowledge Transfer From Only Stimulated Brain Regions for EEG Emotion Recognition.Frontiers in neuroscience · 2022Article
- Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals.Frontiers in neuroinformatics · 2021Article
- Evaluation and Image Analysis of Enterprise Human Resource Management Based on the Simulated Annealing-Optimized BP Neural Network.Computational intelligence and neuroscience · 2021Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Due to a large number of potential applications, a good deal of effort has been recently made toward creating machine learning models that can recognize evoked emotions from one's physiological recordings. In particular, researchers are investigating the use of EEG as a low-cost, non-invasive method. However, the poor homogeneity of the EEG activity across participants hinders the implementation of such a system by a time-consuming calibration stage. In this study, we introduce a new participant-based feature normalization method, named
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