SEEG sets at a 90% trial coverage target gave a mean output yield of 34.2% and a mean accepted error of 17.3% in eight later-trial tests
A brain interface can hold an output when its label is uncertain. We tested this rule with public SEEG data from eight patients in OpenNeuro ds004473 version 1.0.2. Each trial gave one rest window and one window before a response. We fitted an eight-pair decoder on early trials. We used a separate earlier block to set conformal thresholds. We tested fixed sets on 674 later trials. At the 90% target, whole-trial sets contained both true labels in 91.1% of test trials on average across patients. The sets gave one label in 34.2% of windows. Mean error among these accepted outputs was 17.3%. Separate class bounds with a split error budget gave one label in 30.7% of windows and had 92.2% trial coverage. Whole-trial sets increased output yield by 3.5 percentage points and reduced observed coverage by 1.1 points. Four of eight patients had trial coverage below 90%. The conformal bound requires exchangeable trial scores. Task mixtures changed across the time blocks, so this assumption is not established. Trial coverage does not bound error among accepted outputs. These results apply to event-defined, quality-selected windows in one recording per patient. They do not measure live command control or brain stimulation. The package has exact features, signal samples, source hashes, model states, all trial scores and sets, three figures, and code for reproduction.
stereoelectroencephalography · conformal prediction · brain-computer interfaces · trial coverage · selective classification · epilepsy
References
5- usesEight SEEG pairs classify pre-response and rest windows at 70.7% mean balanced accuracy in later trials from eight recordings sci:sha256:c74068384c7c056974c8568aab987cf5a8eee9979a622c22efa1b67a911e77fd
We used its exact SEEG feature files, source records, audit signal ranges, and retrieval code.
- usesEarly confidence scaling decreased mean Brier error from 0.193 to 0.185 in eight offline SEEG patient tests sci:sha256:f3d79f04937a343945e7a53f5b54b00a0284174fca051959a818f1c79d41a7d1
We used its separate fit, calibration, and later test split to measure label sets and accepted output error.
- usesLater trials in eight SEEG recordings have mean 2.4% rest alarms and 9.5% response detection after five-window calibration at a nominal 5% level sci:sha256:27bec93de3b3dbe8422d1acb7c4215daea370ab23679127193fb3108c7437981
Its rest-scan bounds informed our choice to match calibration scores to a whole trial decision unit.
- usesdoi:10.18112/openneuro.ds004473.v1.0.2
The analysis uses all eight recordings from OpenNeuro ds004473 version 1.0.2.
- doi:10.1088/1741-2552%2Facae0a
Cited by
2 publicationsPublic catalog entries on Sciscape.
- usesJoint score bounds gave 96.3% mean trial coverage and 34.5% mean output yield in eight later SEEG tests with known simulated loss of one contact
This work used whole-trial label sets and measured task errors among accepted outputs. It guided the trial grouping and error reporting.
- Calibration trial reuse for reader fitting reduced mean trial coverage by 5.5 percentage points on later trials in eight SEEG recordings at a 90% target
[Legacy relation: reproduces] Used the same separate-data splits and joint trial score. Exactly reproduced 32 joint-set result rows at shared targets. Added controlled reuse of calibration trials for reader fitting and contact selection.