ReviewFrontiers in psychology2026
Complex emotion recognition system using basic emotions via facial expression, electroencephalogram, and electrocardiogram signals: a review.
Review in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Adaptive Multi-Temporal Fusion and Cross-Modal Adversarial Alignment for Robust Driver Fatigue Detection.Sensors (Basel, Switzerland) · 2026Article
- Complex emotion recognition system using basic emotions via facial expression, electroencephalogram, and electrocardiogram signals: a review.Frontiers in psychology · 2026Review
- 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Complex Emotion Recognition System (CERS) deciphers complex emotional states by examining combinations of basic emotions expressed, their interconnections, and their dynamic variations. Through the utilization of advanced algorithms, the system provides profound insights into emotional dynamics, facilitating a nuanced understanding and customized responses. Achieving this level of emotional recognition in machines necessitates knowledge distillation and the comprehension of novel concepts akin to human cognition. The development of artificial intelligence systems for discerning complex emotions poses substantial challenges with significant implications for affective computing. Also, obtaining a sizable dataset for such systems is daunting due to the intricacies involved in capturing subtle emotions, necessitating specialized methods for data collection and processing. Incorporating physiological signals, such as electrocardiograms (ECG) and electroencephalograms (EEG), notably enhances CERS by furnishing insights into users' emotional states, improving dataset quality, and fortifying system dependability. This study presents a comprehensive review assessing the efficacy of machine learning, deep learning, and meta-learning approaches in both basic and complex emotion recognition using facial expressions, EEG, and ECG signals. Selected research papers offer perspectives on potential applications, clinical implications, and results of such systems, intending to promote their acceptance and integration into clinical decision-making processes. Additionally, this study highlights research gaps and challenges in understanding emotion recognition systems, encouraging further investigation by relevant studies and organizations. Lastly, the significance of meta-learning approaches in improving system performance and guiding future research is underscored, with potential applications in universities for advancing educational research, monitoring student well-being, and developing intelligent tutoring systems.
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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.