Design of fMRI-compatible electronic musical interfaces
Bibliographic record
Abstract
The designs of two functional magnetic resonance imaging-compatible electronic interfaces for use in neuropsychological studies involving musical tasks are presented. The devices, a two-button response box in the form of a computer mouse and a piano keyboard, were designed for rhythmic tapping and piano performance tasks, respectively. In order to correlate changes in neural activation acquired through magnetic resonance imaging (MRI) with task performance, the electronic collection of behavioural data, such as the timing of button or key presses was required. These behavioural measures were captured electronically and communicated to a host computer for synchronization with feedback, stimuli, and the MRI scanner itself. As well, data was logged for offline analysis. Due to the intense and volatile electromagnetic fields, most commercially-available electronic interfaces do not function properly and can even pose a serious safety hazard within the MRI scanner environment. Therefore these custom-designed interfaces were free of ferromagnetic parts and all electronic components were relegated to the control room outside of the scanner environment. Acquisition of button and key presses was accomplished using fibre optic sensors, which are immune to electromagnetic interference. The devices performed successfully within the scanner, and MRI scans showed no image artifacts caused by the prototypes. Sensing of key and button transition velocity was sufficient after extensive calibration. Next generation prototypes are planned and will implement more robust and tighter tolerance manufacturing, improved sensing techniques, the acquisition of isometric forces, and an auto-calibration 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".