EEG-to-F0: Establishing artificial neuro-muscular pathway for kinematics-based fundamental frequency control
Bibliographic record
Abstract
The fundamental frequency (F0) of human voice is generally controlled by changing the vocal fold parameters (including tension, length and mass), which in turn is manipulated by the muscle exciters, activated by the neural synergies. In order to begin investigating the neuromuscular F0 control pathway, we simulate a simple biomechanical arm prototype (instead of an artificial vocal tract) that tends to control F0 of an artificial sound synthesiser based on the elbow movements. The intended arm movements are decoded from the EEG signal inputs (collected simultaneously with the kinematic hand data of the participant) through a combined machine learning and biomechanical modeling strategy. The machine learning model is employed to identify the muscle activation of a single-muscle arm model in ArtiSynth (from input brain signal), in order to match the actual kinematic (elbow joint angle) data . The biomechanical model utilises this estimated muscle excitation to produce corresponding changes in elbow angle, which is then linearly mapped to F0 of a vocal sound synthesiser. We use the F0 value mapped from the actual kinematic hand data (via same function) as the ground truth and compare the F0 estimated from brain signal. A detailed qualitative and quantitative performance comparison shows that the proposed neuromuscular pathway can indeed be utilised to accurately control the vocal fundamental frequency, thereby demonstrating the success of our closed loop neuro-biomechanical control scheme.
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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 | 0.000 | 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) | 0.001 | 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".