ArticleSensors (Basel, Switzerland)2023
Real-Time EEG-Based Emotion Recognition.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- [An electroencephalogram-based emotion recognition method using multi-branch convolutional neural networks and Transformer].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Lightweight deep learning model for nonconvulsive status epilepticus diagnosis using EEG time-frequency analysis.Scientific reports · 2026Article
- Continuous Emotion Recognition Using EDA-Graphs: A Graph Signal Processing Approach for Affective Dimension Estimation.Applied sciences (Basel, Switzerland) · 2026Article
- Emotion recognition based on the temporal patterns of electroencephalogram signals and electrodermal response signals using the TRANSFORMER network.Frontiers in neuroscience · 2026Article
- Application of a parametric supermatrix multi-task multimodal deep learning model based on electroencephalogram and peripheral physiological signal fusion in emotion recognition.Frontiers in neuroscience · 2026Article
- A novel deep learning model for objective quantification of generalized anxiety disorder severity using EEG functional connectivity.Frontiers in psychiatry · 2026Article
- Immersive Virtual Reality Environments as Psychoanalytic Settings: A Conceptual Framework for Modeling Unconscious Processes Through IoT-Based Bioengineering Interfaces.Bioengineering (Basel, Switzerland) · 2025Article
- High alpha oscillations in portable prefrontal EEG indicate gender-sensitive biomarkers for emotional disorders.Scientific reports · 2025Article
- Transformer-based deep learning model for predicting fNIRS short-channel signals.Neurophotonics · 2025Article
- Article
- [Dynamic continuous emotion recognition method based on electroencephalography and eye movement signals].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- A Resource-Efficient Multi-Entropy Fusion Method and Its Application for EEG-Based Emotion Recognition.Entropy (Basel, Switzerland) · 2025Article
- Heart rates, facial expressions and self-reports: a multimodal longitudinal approach of learners' emotions in the foreign language classroom.Frontiers in artificial intelligence · 2025Article
- Multidimensional EEG features integration with feature selection strategy for precision diagnosis of depressive disorders.Frontiers in psychiatry · 2025Article
- EEG-based emotional valence and emotion regulation classification: a data-centric and explainable approach.Scientific reports · 2024Article
- Integrating EEG and Ensemble Learning for Accurate Grading and Quantification of Generalized Anxiety Disorder: A Novel Diagnostic Approach.Diagnostics (Basel, Switzerland) · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Most studies have demonstrated that EEG can be applied to emotion recognition. In the process of EEG-based emotion recognition, real-time is an important feature. In this paper, the real-time problem of emotion recognition based on EEG is explained and analyzed. Secondly, the short time window length and attention mechanisms are designed on EEG signals to follow emotion change over time. Then, long short-term memory with the additive attention mechanism is used for emotion recognition, due to timely emotion updates, and the model is applied to the SEED and SEED-IV datasets to verify the feasibility of real-time emotion recognition. The results show that the model performs relatively well in terms of real-time performance, with accuracy rates of 85.40% and 74.26% on SEED and SEED-IV, but the accuracy rate has not reached the ideal state due to data labeling and other losses in the pursuit of real-time performance.
Indexed as
Identifiers
What OpenQuestion holds
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.