Single-trial EEG-based classification reveals Instrument-Specific Timbre Perception via traditional Machine Learning Classifiers
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 monitor brain activity in real-time and adapt their output to the volitions of the user. In music, this could mean selectively amplifying the sound of the instrument the listener wants to hear. The first steps in this research is to determine whether machine learning can be used to identify which instrument an individual is listening to based only on a brief EEG signal. In this work, 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. To distinguish between EEG responses to the five different musical instruments, we investigated the use of three different classifiers – Linear Discriminant Analysis (LDA), Gradient Boosting (GB), and k-NN, and four different sets of features – raw EEG, ERP-based features, harmonics-based features and regularity-based features. N1 and P2 components of the ERP were analyzed for differences between instruments. All three classifiers performed significantly above chance (i.e., approximately 20% for 5 classes) when trained using the raw EEG features (LDA: 37%, GB: 35%, k-NN: 26%). It may be possible to improve these results with more advanced classification algorithms or different transformations of features. Statistical analysis found the Cello to have contributed to the largest P2 amplitude and Pure Tone to the smallest, and for Cello to have contributed to the earliest N1 latency and Clarinet the latest.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".