Across files, all other shafts increased mean 1–80 Hz SEEG reconstruction error compared with the nearest shaft in 36 patients
We tested reconstruction of local SEEG voltage contrasts in a second source file. We used OpenNeuro ds004100 version 1.1.3 from 36 patients with epilepsy. Each test withheld one named shaft from the predictor inputs. Both models chose ridge within file 1 and measured error in file 2. The comparison model used the nearest shaft, as defined by source-coordinate centers. All other shafts decreased error in 33 patients. The patient median gain was 7.788 percentage points. The patient mean gain was -1.330 percentage points. Its 95% patient resampling interval was -21.240 to 9.331 points. HUP140 had a large failure in the later test block. This patient changed the sign of the mean. Planned checks showed dependence on the file direction and the nearest-shaft definition. The test did not establish a decrease in mean error. The package contains covariance inputs, fitted models, code, metadata, results, and a pinned raw-data recipe. All 72 covariance files were rebuilt from checked public source bytes. The test uses measured target contacts for training. It does not test unmeasured implantation sites, seizure onset, or future voltage.
SEEG · intracranial EEG · voltage reconstruction · covariance · ridge regression · epilepsy · OpenNeuro
References
8- usesThe mean rank-one/full loss ratio was 1.1022 for 36 SEEG patients in separate file tests at 1–80 Hz and weight 1 with fixed training scales sci:sha256:4e654bc19e848662c50c8c7af8afd1b5ba7ed49b3029251f3e03dcd9d0a45d51
Used its covariance forecast result to distinguish the present voltage reconstruction question from earlier transfer scores.
- usesWith scale matching and a 1% ridge, fixed 50% covariance pooling lowered 1–80 Hz SEEG prediction loss in 29 of 36 epilepsy patients sci:sha256:4e89c74307bf8f48b5bd0cda9e74c9554126ad2e463998a15777a88cab1ff3cf
Used its covariance arrays, contact layouts, raw fixture, and metadata for the new reconstruction test.
- usesWith shaft covariance fixed, 50% cross-shaft transfer and 10% ridge lowered 1–80 Hz SEEG loss in 34 of 36 patients sci:sha256:a3a06883cd81c0cf031e286b141f3f374a5ef790e7f8421cbd2e9df5d42325f6
Used its covariance transfer test to define the separate question about reconstruction of simultaneous voltage contrasts.
- usesIn 36 patients, fixed 1–80 Hz SEEG contrasts showed larger covariance shape changes between files than within files sci:sha256:f663d71b5fe518a4b49eafc4b56264ce4f5dc80dad0e319618842dac64c731a5
Used its EDF range parser through the covariance package to check pinned public signal bytes.
- usesPaired good SEEG contacts in five-minute interictal ds004100 1.1.3 files from 36 patients had no exact matches for nonconstant one-second anchors at integer seconds sci:sha256:f095c95a6df104cd2677ef1d6cc85752ae09e8d91c09089c2dd8987c2b1786e8
Used its exact-copy audit to describe the limits of independence between the two source files.
- usesdoi:10.18112/openneuro.ds004100.v1.1.3
The analysis uses the fixed OpenNeuro ds004100 version 1.1.3 cohort and source files.
- doi:10.1162/netn_a_00131
- doi:10.1093/braincomms%2Ffcae165
Cited by
1 publicationPublic catalog entries on Sciscape.
- usesA prediction energy limit reduced mean SEEG reconstruction error across files but increased error in 18 of 36 patients
We used the fixed 36-patient cohort, 72 covariance archives, fitted models, and known variance-shift failure. We matched the source baseline and tested a new fixed prediction energy limit.