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Record W4320907019 · doi:10.1016/j.brs.2023.01.261

TMS adaptable auditory control - a universal tool to deal with TMS-evoked auditory potential

2023· article· en· W4320907019 on OpenAlexaff
Matteo Fecchio, Simone Sarasso, Giuseppina Emma Puglisi, Doriana Dal Palù, Andrea Pigorini, Arianna Astolfi, Marcello Massimini, Mario Rosanova

Bibliographic record

VenueBrain stimulation · 2023
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsCanadian Institute for Advanced Research
Fundersnot available
KeywordsTranscranial magnetic stimulationComputer scienceElectroencephalographySpeech recognitionMasking (illustration)Noise (video)Artificial intelligenceStimulationPsychologyNeuroscience

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.020
GPT teacher head0.251
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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