The golden age of online readout: EEG-informed TMS from manual probing to closed-loop neuromodulation
Bibliographic record
Abstract
The integration of transcranial magnetic stimulation (TMS) with electroencephalography (EEG) has markedly enhanced our ability to probe cortical excitability and monitor the brain's electrophysiological responses to external perturbations. In recent decades, this combination has become a widely used and important tool in both basic neuroscience and clinical research. However, persistent challenges remain, particularly the limited reliability of early TMS-evoked potentials (TEPs), contamination from stimulus-locked and induced artifacts (e.g., coil discharge, electrode polarization, cranial muscle activity), and reliance on non-individualized stimulation protocols. This review outlines the evolution of the TMS-EEG methodology in four key implementations: (i) EEG-blind TMS, where stimulation parameters are fixed without EEG-based adjustments; (ii) EEG-informed TMS, which leverages online EEG readouts to optimize stimulation settings prior to acquisition; (iii) EEG-triggered TMS, employing feedforward algorithms to align stimulation with ongoing neural oscillations; and (iv) closed loop TMS, where real-time feedback dynamically adapts stimulation parameters during the session. We examine the electrophysiological and technical foundations of each approach, highlighting their benefits and limitations. Emerging closed-loop systems represent a shift toward adaptive, data-driven neuromodulation, unlocking promising avenues for personalized brain stimulation. Further refinement of these approaches will be critical to improving their precision, reliability, and applicability in diverse clinical and research settings. Collectively, these developments demonstrate a field-wide progression toward increasingly precise and individualized brain stimulation strategies, enabled by real-time electrophysiological feedback and customizable stimulation protocols.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".