A Real-Time Phase-Locking System for Non-invasive Brain Stimulation
Bibliographic record
Abstract
Non-invasive brain stimulation techniques are entering widespread use for the investigation and treatment of a range of neurological and neuropsychiatric disorders. However, most current techniques are 'open-loop', without feedback from target brain region activity; this limitation could contribute to heterogeneous effects seen for nominally 'inhibitory' and 'excitatory' protocols across individuals. More potent and consistent effects may ensue from closed-loop and, in particular, phase-locked brain stimulation. In this work, a closed-loop brain stimulation system is introduced that can analyze EEG data in real-time, provide a forecast of the phase of an underlying brain rhythm of interest, and control pulsed transcranial electromagnetic stimulation to deliver pulses at a specific phase of the target frequency band. The technique was implemented using readily available equipment such as a basic EEG system, a low-cost Arduino board and MATLAB scripts. The phase-locked brain stimulation method was tested in 5 healthy volunteers and its phase-locking performance evaluated at 0, 90, 180, and 270 degree phases in theta and alpha frequency bands. On average phase locking values of 0.55° ± 0.11° and 0.52° ± 0.14° and error angles of 11° ± 11° and 3.3° ± 18° were achieved for theta and alpha stimulation, respectively. Despite the low-cost hardware implementation, signal processing time generated a phase delay of only 3.8° for theta and 57° for alpha stimulation, both readily accommodated in the pulse trigger algorithm. This work lays the methodological steps for achieving phase-locked brain stimulation for brief-pulse transcranial electrical stimulation (tES) and repetitive transcranial magnetic stimulation (rTMS), facilitating further research on the effect of stimulation phase for these techniques.
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How this classification was reachedexpand
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".