Nine shifted templates increased mean site accuracy under nine equally weighted simulated timing offsets from −20 to +20 ms in five offline SEEG sessions
We tested site identification from public SEEG stimulation responses in OpenNeuro ds004457 version 1.0.2. The query window was 40 to 120 ms. We used four calibration pulses and four separate test pulses per site. Five sessions supplied 183 sites and 732 unique test pulses. We tested nine common timestamp offsets from -20 to +20 ms in 5 ms steps. Each offset and patient had equal weight. Nine shifted calibration templates increased mean site accuracy from 17.79 to 30.90 percent. All five sessions showed an increase. At zero offset, mean accuracy decreased from 32.93 to 30.99 percent. The patient sign-flip test gave p = 0.0625. A later response window gave a smaller increase. A before-pulse control stayed near chance. All 110 fresh source files matched their pinned hashes. Raw reconstruction and independent distance calculations reproduced the archived results. The package includes data, code, figures, decisions, provenance, and reproduction instructions. This is a retrospective test with reused patients and synthetic offsets. It does not establish online interface performance or effective brain writing.
stereoelectroencephalography · SEEG · electrical stimulation · site identification · timing offset · calibration templates · offline benchmark
References
4- usesCovariance angles kept 37.9 percent mean site accuracy under nine simulated positive common voltage gains in five offline SEEG sessions sci:sha256:0dbd4e1ba615799ff752cce0c46663f54b7af5ebdf8dd5a55d3ee4cebeb486e4
We used its archived contact bins, event tables, target contacts, source manifest, and eligibility rules. We tested new timing-offset scenarios and computed new predictions. We did not reuse its gain-scenario predictions.
- usesA fixed covariance rule gave higher SEEG site accuracy than a diagonal rule in offline tests from five patient sessions sci:sha256:1407cd9ac712a4bcd551a3738cc90c3fab309b871572cbd6fc644b41555f7886
We adapted its MIT raw MEF extraction code, supplied through the direct parent package. Fresh raw reconstruction checked all retained contact-bin arrays against the archived inputs.
- usesdoi:10.18112/openneuro.ds004457.v1.0.2
The analysis uses the archived SEEG stimulation responses and metadata from OpenNeuro ds004457 version 1.0.2.
- doi:10.1523/JNEUROSCI.1325-22.2023
Cited by
1 publicationPublic catalog entries on Sciscape.
- A 90% timing prior gave no clear accuracy gain over nine shifted SEEG templates in five offline sessions
[Legacy relation: reproduces] We exactly reproduced the one-template and maximum-shift mean accuracy under nine equally weighted offsets, and their correct-time mean accuracy. We used its nine-offset feature bank as the comparator for a fixed likelihood mixture. This is the same cohort, not an independent patient replication.