Comprehensive Review of Physiological Signal-Based Emotion Recognition: Methods, Challenges, and Insights on Arousal and Valence Dimensions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This review examines emotion recognition from physiological signals, focusing on processing methods and classification techniques. Key aspects include signal acquisition challenges, emotion elicitation strategies, and feature extraction methods across time, frequency, and time-frequency domains. The analysis highlights the strengths of these methods in capturing emotional information from signals such as ECG, GSR, RSP, SKT, BVP, EMG, and EOG. Feature selection and dimensionality reduction are explored regarding their role in optimizing the feature space for classification. The review also evaluates machine learning approaches and their applications in emotion recognition. This work addresses current capabilities, limitations, and emerging trends and provides a comprehensive overview of physiological signal-based emotion recognition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it