TMS Adaptable Auditory Control: a universal tool to mask TMS click
Bibliographic record
Abstract
Abstract Coupling transcranial magnetic stimulation with electroencephalography (TMS-EEG) allows to non-invasively probe the human thalamocortical system. However, obtaining a pure TMS evoked potential requires controlling for several confoundings, such as auditory potentials evoked (AEPs) by the TMS 'click'. An effective control for AEPs consists of playing a noise through earphones in order to mask the TMS 'click' (masking noise).Here we tested TMS Adaptable Auditory Control (TAAC), a tool that generates in real-time a masking noise adapted to the stimulator and optimized to fit the ‘click’ perception. To this aim, we systematically measured the sound level required to mask the TMS ‘click’ with the state-of-the-art masking noise (i.e. white noise and adapted noise) and with two masking noises generated through TAAC played through in-ear earphones in a group of 19 healthy subjects. TMS pulses were delivered while gradually increasing the loudness until subjects were not able to identify the TMS 'click'. We iteratively performed this procedure for all four masking noises and during both a real TMS (2 cm behind the vertex) and a sham TMS condition (on the vertex, 90° sagittal rotation). Finally, we estimated the equivalent sound level for each masking noise as measured through a Head and Torso Simulator (HATS, Bruel&Kjaer, Type4128). Results show that all masking noises were effective at the following sound levels (Medians): White noise=87.1Db; Adapted noise=88.9Db; TAAC Noise1=84.1Db; TAAC Noise2=82.9Db. Thus, we demonstrate that the sound level required to mask TMS 'click' is safe for the human auditory system for the typical duration of TMS-EEG procedures with respect to standard safety tables (e.g. 90Db for 2 hours). Furthermore, TAAC can be used to mask TMS 'click' at lower sound levels compared to currently available masking noises. Keywords: TMS-EEG, Noise masking, Auditory evoked potential, TEP
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".