Two nearby donors reduced withheld SEEG slope error by 18.8% versus shaft means across record ends in 24 participants at 20–100 Hz without 55–65 Hz
We tested whether contact order helps predict a withheld SEEG spectral slope. We used public OpenNeuro ds005624 version 1.0.0, with 34 valid records from 24 participants. We fitted log10 power against log10 frequency at 20–100 Hz and removed 55–65 Hz. All selected bipolar pairs had distinct contacts. The mean first-minute slope of two nearby donors predicted each last-minute target. The reference used all other retained pairs on the same shaft. Both methods excluded the target's earlier data. Error was averaged within shafts, records, and participants. The nearby method reduced mean squared error from 0.919319 to 0.746895, a reduction of 18.8 percent. The 95 percent participant bootstrap interval was 11.8 to 25.3 percent; inference assumes independent and exchangeable participants. Error fell in 21 of 24 participants. A nearby similarity pattern was unclear after removal of a shaft linear trend. The target's own earlier slope gave a larger reduction when available. The result supports a simple method for withheld features in this reused clinical cohort. It does not identify a neural cause. The package preserves inputs, code, source versions and hashes, donor tables, uncertainty draws, raw checks, and reproduction instructions.
SEEG · intracranial EEG · spectral slope · contact order · forecasting · spectral analysis · electrophysiology · OpenNeuro
References
4- usesA quadratic log-frequency model lowers SEEG shape error between the first and last minute in 24 participants at 20–100 Hz with 55–65 Hz removed sci:sha256:10c57f5b0211dec06505fe654fde6063e899c4d880876ceb72b215b7dee8baa4
This study used the unchanged 35 compact spectrum files, exact source versions and range hashes, source metadata, extraction code, and source audit code. It added a new comparison of nearby donors with all shaft donors for a withheld spectral slope.
- usesAn equal mix of pair and shaft curvature reduced SEEG shape error by 16.1 percent between record ends in 24 participants at 20–100 Hz with 55–65 Hz removed sci:sha256:9b66f9ecd0695e104904627d2012a872d88daa04d799b1bb1ddfaa0200a796a4
This study used the prior shaft curvature question and contact exclusion approach to define the donor comparison. The earlier numerical results were known before the local plan, but are not numerical inputs to this analysis.
- usesdoi:10.18112/openneuro.ds005624.v1.0.0
Source dataset used for the SEEG records analyzed in this study; the cited DOI is for the exact version 1.0.0 used.
- doi:10.1038/s41593-020-00744-x
Cited by
4 publicationsPublic catalog entries on Sciscape.
- usesSpectral shape gave 66.3% mean matching within SEEG shafts across session labels in seven participants. Equal names were assumed to identify equal contacts.
We used its unchanged spectra from 17 records, source metadata, source pins, and extraction code. We tested contact identity across session labels.
- Two nearby donors reduced withheld SEEG slope error by 25.1% across files in 20 ds004100 patients. Fits used 20–100 Hz and omitted 55–65 Hz.
[Legacy relation: reproduces] This study uses the fixed two-donor rule. It tests a second dataset after equal patient IDs are excluded. The prior file list supplied the IDs to exclude.
- usesFilter response correction reduced the forward and backward 60 Hz notch slope difference from unfiltered SEEG by 99.6% in 24 participants at 20–100 Hz with Q=30 and 55–65 Hz excluded
This publication supplied source range versions and hashes, channel and pair checks, the fixed disjoint mask, extraction code, and compact reference spectra. We selected one valid session per participant and processed fresh raw ranges.
- usesCurrent changes on other shafts reduced last-minute SEEG slope error by 4.3% when first-minute slopes were known in 24 participants at 20–100 Hz with 55–65 Hz removed
This publication supplied the contact-disjoint pair rule and the minimum pair and shaft counts. We used those selection rules. Our endpoint keeps the earlier target slope and adds current changes from other shafts. The earlier spatial prediction results were not numerical inputs.