The rt-TEP tool: real-time visualization of TMS-Evoked Potentials to maximize cortical activation and minimize artifacts
Bibliographic record
Abstract
The impact of transcranial magnetic stimulation (TMS) on cortical neurons is currently hard to predict based on a priori biophysical and anatomical knowledge alone. Lack of control of the immediate effects of TMS on the underlying cortex can hamper the reliability and reproducibility of protocols aimed at measuring electroencephalographic (EEG) responses to TMS. We introduce and release a novel software tool labelled rt-TEP (real-time TEP). This tool interfaces with different EEG amplifiers and offers a series of informative visualization modes to assess the magnitude of the initial brain response to TMS and the overall quality of TMS-evoked potentials (TEPs) in real time. We show that rt-TEP can be used to detect - and thus abolish or minimize - magnetic and muscle artifacts contaminating the post-stimulus period of single-trial data: this application affords a clear visualization and quantification of the amplitude of the early (8-50 ms) and local EEG response after averaging a limited number of trials. Such real-time readout can then be used to optimize TMS parameters (e.g., site, orientation, intensity) before data acquisition to obtain TEPs characterized by high signal-to-noise ratio. The ensemble of real-time visualization modes of rt-TEP are not currently implemented in any available commercial software and provide a key readout to titrate TMS parameters beyond the a priori information provided by biophysical and anatomical models. Real-time optimization of TMS parameters to achieve a desired level of initial activation can facilitate the acquisition of reliable TEPs and can improve the reproducibility of data collection across laboratories.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".