What is the Best Music for Neurofeedback Training?
Bibliographic record
Abstract
The nucleus accumbens (NAc) is widely known for its role in reward seeking behavior which is heavily reliant on dopamine signaling. Dopamine plays an important role in reward seeking behavior and motivation and its dysregulation is shown to cause symptoms of depression such as apathy, a lack of motivation, and anhedonia, a loss of pleasure. Studying the NAc, specifically the ventral striatum in this case, is critical in understanding the underlying mechanisms of this dysregulation. Music neurofeedback is a biofeedback technique that provides feedback on brain activity through the audio quality of the music, with lower quality sounding more muffled. This study uses this technique to train participants to increase the activity of their ventral striatum in an attempt to upregulate the activity of the reward system. This paper aims to investigate what the most effective music choices are in terms of genre, key, valence (positive or negative emotions) and energy (pertaining to levels of arousal) to maximize the improvement on neurofeedback training. These musical attributes were identified by the Spotify “Organize Your Music” categorization tool. Participants underwent six EEG music neurofeedback training sessions with individually tailored pleasurable music as the source of feedback. The participants either received real feedback in the neurofeedback group (NF) or sham feedback in the control group. The results of this study showed that neurofeedback performance was negatively correlated with valence whereby songs that led to the greatest performance, measured as increase in ventral striatum activity from baseline, were those low in valence. Participants that had the greatest improvement in their neurofeedback training selected songs that were in a minor key and belonged to the pop genre. Although this study is based on a small sample, it takes the first step towards the overarching goal of using music to manage dysregulation in the reward system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".