Decoding Musical Timbre Perception from Single-Trial EEG Data
Bibliographic record
Abstract
Many users of hearing aids report challenges when listening to music. In the future, it may be possible to develop hearing aids that have electrodes which monitors brain activity in real-time and adapts the filters on the hearing aid to match the volitions of the user. In music, this could mean amplifying the sound of the instrument the listener wants to hear. One of the first steps in this research is to determine if a machine learning algorithm can identify to which instrument an individual is listening based only on a brief EEG signal. To test this possibility, participants were presented with a series of brief tones that varied in timbre (Trombone, Clarinet’ Cello, Piano and Pure Tone) while their ongoing EEG was recorded from 73 electrodes. Linear Discriminant Analysis (LDA) was used. We investigated four different sets of features - Raw EEG, ERP-based features, Harmonics-based features and Regularity-based features. The Raw EEG based classifier performed significantly above chance (37%) when attempting to distinguish between responses to different musical instruments for 5-way classification. More advanced classification algorithms or different features may be able to better distinguish between tones with a musical timbre.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".