SEEG power after key presses reduced error prediction loss beyond timing in separate blocks of four recordings
We tested rare key errors in OpenNeuro ds004473 version 1.0.2. An error was a valid key that differed from the arrow cue. Four recordings had errors in at least three task blocks. They supplied 1,057 trials and 28 errors. We tested each model on a separate task block. Inner block tests selected signal pairs and model settings. Adding 70 to 110 Hz SEEG power from 0.05 to 0.55 seconds after the key press reduced mean log loss beyond a timing model. Loss fell from 0.12864 to 0.09934 nats per trial, a 22.8% reduction. Total loss fell in all four recordings. In one recording, error-trial predictions became worse while agreement-trial predictions improved. Removing responses below 0.15 seconds preserved the mean gain. Before-key and before-fixation power had smaller mean gains. The two-sided patient sign-flip test gave p = 0.125. These results do not establish a population effect or live BCI correction. The package preserves code, features, source hashes, fitted models, every test probability, three figures, and reproduction instructions. Fresh public EDF ranges reproduced all 33 retained error trials and eight agreement trials exactly.
stereoelectroencephalography · rare error prediction · logistic regression · block validation · binary log loss · brain-computer interface
References
3- usesEight public SEEG recordings contain 33 trials with opposite cue and key labels after fixed signal checks sci:sha256:c34131cf98ace6df622cbef2ed6db5656ea040b65d2ec737cda6429222533319
This archive supplied the preserved SEEG features, cue and key labels, source metadata, and extraction code. We checked its input hashes and rebuilt public ranges for all 33 retained errors and eight agreement trials before testing rare-error models.
- doi:10.1088/1741-2552%2Facae0a
- usesdoi:10.18112/openneuro.ds004473.v1.0.2
The publication analyzes OpenNeuro ds004473 version 1.0.2 and retains pinned source recipes and source hashes.
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 its fixed SEEG features, source metadata, raw examples, and fitting helper code. Its block tests allowed later training blocks. This study instead used past source blocks and fixed alert costs.
- usesA fixed SEEG reader gave a 46.9% conditional key rank after 32 cue-key errors in five people
Its error detection question informed the interpretation limit: matched score differences can reflect error-related activity without establishing a key representation. We use no classifier or result table from that study.
- usesSEEG power lowered mean later error loss in seven records, with 90 false alerts at a 0.05 probability threshold
Used its fixed SEEG features, source metadata, raw examples, and fitting helper code. Its block tests allowed later training blocks. This study instead used past source blocks and fixed alert costs.