Using prefrontal cortex near-infrared spectroscopy and autonomic nervous system activity for identifying music-induced emotions
Bibliographic record
Abstract
Physiological-based emotion identification systems may offer an alternative means of expressing emotions, particularly, for adults and youth with severe motor disabilities who may have little or no voluntary muscle control. The current study investigated inclusion of autonomic nervous system activity in combination with central nervous system activity in the form of a multi-modal emotion identification system. Prefrontal cortex hemodynamics were monitored using near infrared spectroscopy, and autonomic nervous system activity (ANS) was concurrently monitored using heart rate, skin temperature and electrodermal activity sensors, in a music-based emotion induction paradigm. Classifiers were trained using ANS or prefrontal hemodynamic features, in addition to dynamic modeling-based features. A combination of classifier decisions was applied for solving arousal (intense vs. neutral) and valence (positive vs. negative) classification problems. The classification accuracies of the ensemble varied substantially across participants (54.4%–85.1% for the arousal differentiation and 48.4%–76.8% for the valence differentiation). These results suggest the importance of individual specific detection algorithms in physiological-based emotion identification efforts. In addition, combining features from the autonomic and central nervous system resulted in a degradation of classification accuracies (68.3% in arousal and 58.5% in valence differentiation) compared to when prefrontal hemodynamic features were used exclusively (71.9% for both arousal and valence differentiation).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".