Adding SEEG power did not show a clear wrong-key ranking gain over timing in six recordings with fixed block calibration
We tested whether SEEG power after a response improves wrong-key ranking over task timing. We used OpenNeuro ds004473 version 1.0.2. The fixed sample had 2,012 valid-key trials in eight recordings. Six recordings had 33 wrong-key events. Calibration used cue-key agreement trials from other source task blocks. The model used eight bipolar pairs and three power bands. The primary score added timing and SEEG scores with equal weights. Mean within-block AUC was 0.660 for timing and 0.673 for the combined score. The gain was 1.34 percentage points. A descriptive patient bootstrap interval ran from -1.41 to 4.35 points. The exact patient sign-flip value was 0.500. A control with 200 label draws and full model refits gave 0.358. These tests did not establish a gain. We rebuilt all 33 wrong-key trials and eight agreement trials from pinned public EDF ranges. The rebuilt features matched the inputs exactly. A separate full analysis run reproduced all saved numerical results. The package preserves source features, labels, fitted models, scores, control data, figures, code, and retrieval instructions. The test used physical key responses and offline calibration. It did not test live BCI use or brain stimulation.
stereoelectroencephalography · wrong-key detection · anomaly scoring · brain-computer interface · within-block AUC
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 the five fixed spectral feature windows, source metadata, and bounded EDF retrieval code. These files supplied pinned inputs and a raw feature check recipe.
- usesEight public SEEG recordings contain 33 trials with opposite cue and key labels after fixed signal checks sci:sha256:c34131cf98ace6df622cbef2ed6db5656ea040b65d2ec737cda6429222533319
We used the audited cue and key labels, retained feature sample, and raw check records. These inputs enabled a new anomaly comparison that excluded wrong-key calibration examples.
- doi:10.3389/fnins.2019.00502
- usesdoi:10.18112/openneuro.ds004473.v1.0.2
Source dataset, OpenNeuro ds004473 version 1.0.2, supplied the analyzed recordings and event metadata.
- doi:10.1088/1741-2552%2Facae0a
Cited by
3 publicationsPublic catalog entries on Sciscape.
- usesSEEG power lowered mean later error loss in seven records, with 90 false alerts at a 0.05 probability threshold
Used the prior anomaly test to identify the need for past-block tests with rare errors. Its models were not used for prediction here.
- usesPower from eight SEEG pairs before known response times gave 44.7 percent mean key agreement on 33 cue conflict trials in six recordings
Prior wrong-key ranking work supplied the same valid cue conflict definition and known count of 33 cases. We tested a different target: recorded key identity rather than an error score. No conflict trial entered model fitting.
- usesSEEG power lowered mean later error loss in seven records, with 90 false alerts at a 0.05 probability threshold
Used the prior anomaly test to identify the need for past-block tests with rare errors. Its models were not used for prediction here.