Automatic detection of a prefrontal cortical response to emotionally rated music using multi-channel near-infrared spectroscopy
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Bibliographic record
Abstract
Emotional responses can be induced by external sensory stimuli. For severely disabled nonverbal individuals who have no means of communication, the decoding of emotion may offer insight into an individual's state of mind and his/her response to events taking place in the surrounding environment. Near-infrared spectroscopy (NIRS) provides an opportunity for bed-side monitoring of emotions via measurement of hemodynamic activity in the prefrontal cortex, a brain region known to be involved in emotion processing. In this paper, prefrontal cortex activity of ten able-bodied participants was monitored using NIRS as they listened to 78 music excerpts with different emotional content and a control acoustic stimuli consisting of the Brown noise. The participants rated their emotional state after listening to each excerpt along the dimensions of valence (positive versus negative) and arousal (intense versus neutral). These ratings were used to label the NIRS trial data. Using a linear discriminant analysis-based classifier and a two-dimensional time-domain feature set, trials with positive and negative emotions were discriminated with an average accuracy of 71.94% ± 8.19%. Trials with audible Brown noise representing a neutral response were differentiated from high arousal trials with an average accuracy of 71.93% ± 9.09% using a two-dimensional feature set. In nine out of the ten participants, response to the neutral Brown noise was differentiated from high arousal trials with accuracies exceeding chance level, and positive versus negative emotional differentiation accuracies exceeded the chance level in seven out of the ten participants. These results illustrate that NIRS recordings of the prefrontal cortex during presentation of music with emotional content can be automatically decoded in terms of both valence and arousal encouraging future investigation of NIRS-based emotion detection in individuals with severe disabilities.
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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 | 0.001 |
| 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) | 0.000 | 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