Five-pulse maps used fewer waveform values without a clear 20–500 ms SEEG error gain over noise scaling in six epilepsy sessions
We tested shared time patterns across SEEG recording pairs at 66 stimulation sites in six people with epilepsy. Five early pulses set each map. Five later pulses tested centered voltage from 20 to 500 ms. The mean error was 1.80% higher than separate noise-scaled pair means. The 95% person bootstrap interval for error gain was -3.60% to 0.20%. The maps used 60.0% of the direct maps' stored waveform values under factor storage. The package contains exact cross-pair voltage products, native examples, source checks, code, and figures. The result concerns one session per person and does not establish online or clinical benefit.
SEEG · electrical stimulation · brain-computer interfaces · voltage forecasting · low-rank models · signal processing
References
5- usesPower weights fit from five pulses gave no clear mean 20–500 ms SEEG voltage forecast gain in eight sessions sci:sha256:5f8ad290d211abb81202a82ed21683bfe60c8df22cceb4e18c340cf2fc14ee28
Eligible pair and pulse maps, fixed source receipts, metadata, and source retrieval code enabled the new cross-pair products and later voltage forecasts.
- usesA fixed update from one SEEG readout increased centered voltage error on other shafts at 20 to 500 ms in eight patient sessions after five calibration pulses sci:sha256:2c4e81a6206f7e990b865c20793285c245d4f49983068585107cffb8836b9da5
The early-pulse off-diagonal product formula supplied the separate noise-scaled mean reference.
- usesdoi:10.18112/openneuro.ds004696.v1.0.1
Source data: OpenNeuro ds004696 version 1.0.1.
- doi:10.1523/JNEUROSCI.2201-22.2023
- doi:10.1371/journal.pcbi.1011105
Cited by
1 publicationPublic catalog entries on Sciscape.
- usesMaximum-error selection increased later worst-pair error in five-pulse SEEG maps from six epilepsy sessions
This work supplied the fixed six-session joint voltage products, site maps, source receipts, and mean-error selection method. We reproduced its mean-selection choices and losses before testing maximum-error selection.