A fixed joint SEEG model increased mean normalized error compared with separate scaling after five calibration pulses at 20–500 ms in eight sessions
We tested whether a joint spatial rule improved a five-pulse forecast of later individual SEEG responses. The source was OpenNeuro ds004696 version 1.0.1. Eight patients supplied 86 stimulation sites and 886 remote bipolar readouts. At 20–500 ms, the fixed joint rule increased mean error by 2.46% against separate readout scaling. Error was divided by each readout's early pulse energy. The descriptive 95% patient interval for the increase was 1.50% to 3.51%. All eight patients had higher normalized error. Voltage-squared error increased by 2.94%. The package adds exact cross-readout products, model code, source checks, figures, and a public retrieval recipe. Spatial denoising is established. This is a new fixed test on reused clinical data. It does not show online control or a better stimulation outcome.
stereo-EEG · SEEG · spatial denoising · single-pulse forecasting · intracranial electrophysiology · signal processing
References
7- Five-pulse noise scaling reduced later single-pulse error for 20–500 ms centered bipolar SEEG in eight patient sessions sci:sha256:fbc06ab469d48eac7a6f54dd97cd24ccd9c7c5ca4b0db4d64340536b3e81428a
[Legacy relation: reproduces] We independently calculated the separate readout scaling rule from the inherited ten-pulse products. We reproduced its mean normalized error reduction of 6.66% against the full early mean on the same eight sessions. That rule was the primary comparison for the fixed joint spatial model.
- usesA five-pulse check selected more repeatable bipolar SEEG pairs within eight epilepsy patient sessions sci:sha256:b5b7963746f5c6d2a2a8f7844ec590732a4eb152bc7a70979b3bc95c3e27820f
We used the original rules for good pulses, constant current, remote adjacent bipolar readouts, and the first five and final five pulses. The checked archive carried these selections forward. We did not change selection in response to the new spatial results.
- usesFour-bit storage reduced waveform payload by 87% and increased mean normalized 20–500 ms forecast error by 0.70% compared with float32 in eight SEEG sessions sci:sha256:1e8bc16598da030977cb3c6aa2ae9c6810a604515218a1685994c7fde5e81623
This archive supplied the stimulation site and readout selection tables, exact within-readout pulse products, source metadata, and the source retrieval code. We rebuilt every inherited pulse product from the public source and added exact cross-readout products for a fixed spatial forecast test.
- usesdoi:10.18112/openneuro.ds004696.v1.0.1
Source data: OpenNeuro ds004696 version 1.0.1; the saved version-specific metadata records CC0.
- usesdoi:10.1523/JNEUROSCI.2201-22.2023
Data paper for the OpenNeuro source dataset used in this analysis.
- usesdoi:10.1371/journal.pcbi.1011105
Describes the CRP source method associated with the author-supplied trial arrays; this study did not use fitted CRP waveforms, significance, or scores.
- doi:10.1016/j.jneumeth.2008.03.015
Cited by
3 publicationsPublic catalog entries on Sciscape.
- supportsA fixed donor model raised 20–500 ms SEEG error for simulated shaft loss after five fit pulses in eight people
Its fixed joint template had more mean normalized error than separate scaling after five fit pulses in the same cohort. Our new test also favors the scaled template, but tests a different input: signals from other shafts on the same later pulse. The two tests are not independent cohorts.
- usesA fixed spatial noise rule gave no clear 20 to 500 ms AUC gain after five early pulses in eight SEEG sessions
The range download routine was adapted. Source operation and chronology records supported the new extraction. This study tests response-window AUC, while that study tested waveform forecast error.
- usesFixed noise pooling did not establish lower mean SEEG forecast error at 20–500 ms after five calibration pulses in eight sessions
Its fixed joint spatial model increased error. This result informed the present question about sharing a scalar noise fraction while keeping each readout waveform separate. We did not reuse its spatial model or cross-readout products.