After five calibration pulses, about 25 ms template offsets gave a 7.48% mean patient error increase in 100–500 ms SEEG forecasts across eight sessions
We tested fixed timing errors in a five-pulse waveform forecast. We used public stereoelectroencephalography (SEEG) from eight patients in OpenNeuro ds004696 version 1.0.1. The test used 86 stimulation sites, 886 site-to-readout pairs, and 430 distinct later pulses. The target was centered bipolar voltage from 100 to 500 ms after a pulse. Each pair used five early pulses to set a mean waveform and a noise scale. We shifted template sampling by plus or minus 51 samples, or 24.90 ms. The final five pulses tested each forecast. We divided each loss by its unchanged calibration trial energy. We then gave sites and patients equal weight. Mean patient error increase was 7.48%. The descriptive 95% patient interval was 4.78% to 10.32%. The exact two-sided patient sign-flip value was 0.0078125. Error increased in 8 of eight patients. A control that kept template energy fixed gave 7.09% mean error increase. The package preserves exact products for seven offsets, all fits and losses, source hashes, code, and three figures. All eight full source files passed hash checks. Direct waveform errors matched the compact calculations. This is an offline timing sensitivity test in one session per patient. It does not test natural clock errors, a causal neural effect, or live BCI control.
stereoelectroencephalography · brain-computer interface · electrical stimulation · waveform forecast · timing offset · signal processing
References
5- usesFive-pulse noise scaling reduced later single-pulse error for 20–500 ms centered bipolar SEEG in eight patient sessions sci:sha256:fbc06ab469d48eac7a6f54dd97cd24ccd9c7c5ca4b0db4d64340536b3e81428a
We used the calibration noise-scale method and extraction code inherited through the direct parent. We held this scale fixed while testing seven template sampling offsets.
- usesA five-pulse check selected more repeatable bipolar SEEG pairs within eight epilepsy patient sessions sci:sha256:b5b7963746f5c6d2a2a8f7844ec590732a4eb152bc7a70979b3bc95c3e27820f
We used the adjacent-contact bipolar and spatial exclusion rules inherited through the direct parent. The source rebuild checked these fixed contacts and excluded each stimulated shaft.
- usesA five-pulse test did not establish lower SEEG size prediction error from pre-pulse RMS in eight patient sessions sci:sha256:947fe7aa6e242e0f9e723e54cb14b472f7f801ecbaac8501e809356a1e6953c5
We used the fixed sample of 86 sites and 886 bipolar readouts, the chronological five-pulse split, retrieval code, source metadata, and late-window trial products. The new source rebuild matched these products exactly for all eight patients.
- usesdoi:10.18112/openneuro.ds004696.v1.0.1
The fixed analysis used source-author trial arrays from OpenNeuro ds004696 version 1.0.1; the publication records the snapshot commit and per-file hashes.
- doi:10.1523/JNEUROSCI.2201-22.2023
Background source study describing electrical stimulation driven network interactions in the human limbic system; it motivates the late-window test but does not supply its timing result.
Cited by
4 publicationsPublic catalog entries on Sciscape.
- usesEarly timing fits increased mean relative late SEEG forecast error by 1.92 percent compared with amplitude fits in five sessions after four calibration pulses per known site
Its test of imposed late template offsets motivated an early-only estimate of an offset for a later response forecast. The datasets and estimands differ.
- usesA 25 ms search raised window AUC under uniform simulated ±25 ms errors at 86 sites in eight SEEG sessions
Its test of template time offsets motivated the clock question. We tested a different endpoint: late pulse/background AUC under uniform simulated errors with a matched time search. The current result is not a replication of its waveform forecast endpoint.
- usesTraining with nine window shifts raised mean site accuracy under uniform −20 to 20 ms shifts but reduced accuracy at zero in five SEEG sessions
Its offset test in late voltage forecasts motivated the window alignment question. We tested site classification and training with shifted windows in a different public dataset.
- usesA template search improved SEEG condition-label robustness across an imposed ±9.77 ms error grid in eight patients
Its imposed timing-error test motivated the question. This study tests condition labels with a different dataset and reader. No source data or model code were reused.