The rt-TEP tool: real-time visualization of TMS-Evoked Potentials to maximize cortical activation and minimize artifacts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it