TMS adaptable auditory control - a universal tool to deal with TMS-evoked auditory potential
Bibliographic record
Abstract
Abstract Coupling transcranial magnetic stimulation with electroencephalography (TMS-EEG) allows recording the EEG response to a direct, non-invasive cortical perturbation. However, obtaining genuine TMS-evoked potentials (TEPs) requires controlling for several confounds, including auditory evoked potentials (AEPs) associated with the TMS discharge noise (TMS click). To rule out AEPs from TEPs, AEP can be mathematically removed from contaminated TEP through off-line pre-processing, or early prevented by masking the TMS click with a noise during the TEPs acquisition. Although more effective, the use of the latter is limited due to the risks associated with delivering loud noises to subjects. We tested and released TMS Adaptable Auditory Control (TAAC), an open-source Matlab®-based tool that allows users to generate customized masking noises designed on the coil-specific TMS click. Notably, to create a noise tailored to the subject-specific click perception, TAAC mixes and manipulates in the time and frequency domains two standard noises used in TMS literature (i.e., a white noise and a noise adapted from the TMS click). Here we compared two customized noises generated through TAAC to both white and adapted noise in a population of 20 healthy subjects by quantifying the sound pressure level (SPL, in dB, measured with a Head and Torso Simulator) required to mask the TMS click. Both TAAC customized noises were effective at safe and lower SPLs with respect to standard noises, according to safety guidelines. By minimizing sound loudness, TAAC generates effective and safe masking noises customized to each TMS device and tailored to single individuals. Associating TAAC with a tool for real-time visualization of TEP waveforms can help control the masking procedure’s effectiveness in non-compliant patients. Finally, TAAC is a highly flexible tool, so its applicability can be extended to meet different experimental setups that may be affected by acoustic startle responses, such as during TMS-electromyography. Research Category and Technology and Methods Basic Research: 10. Transcranial Magnetic Stimulation (TMS) Keywords: Auditory evoked potentials, Noise Masking, TMS-EEG, AEP
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".