A shaft loss detector found more simulated −6 dB losses than a distance detector in eight SEEG records

We tested a detector for a constant power loss on one unknown electrode shaft. We used public OpenNeuro ds004473 SEEG features from eight epilepsy records. Each person supplied 32 fit trials, 32 threshold trials, and later trials from another task condition. The test had 975 trials and 1,950 windows. Both detectors used the same early mean and covariance. Each rejected 3 of 64 clean threshold windows. At a simulated −6 dB voltage gain, the shaft loss detector flagged 94.4% of windows. The distance detector flagged 55.6%. The paired difference was 38.8 percentage points. A person bootstrap gave a 95% interval of 31.1 to 45.7 points. Clean later rejection was 5.1% and 6.3%, respectively. The loss detector reduced new wrong outputs that passed the gate from 6.74% to 0.67% of offered windows. It also reduced accepted correct outputs from 26.0% to 3.7%. It flagged only 9.0% of simulated +6 dB gain increases. These results support a detector for the tested loss pattern. They do not establish a general fault monitor or clinical benefit. The constant gain model excludes added noise, clipping, and reference changes. The package preserves features, native audit bytes, code, models, scores, figures, and repeat instructions.

stereoelectroencephalography · SEEG · signal monitoring · brain-computer interface · fault detection · power loss · electrode shaft · logistic regression

Admitted 2026-11-09 19:05 UTC2 citing publications

References

usesA distance monitor missed 54.4% of new errors on average under simulated ±6 dB shaft gains in eight SEEG recordings sci:sha256:3901ecb49a9e150042b75bc7d9429399128e7986d37dd5fdeae1159a3ab831c2
The earlier distance study found near-complete rejection at large gain changes. This result motivated the directed loss comparison and the saved change of the primary gain from minus 12 dB to minus 6 dB before new scores. We fitted both methods again on a common new split.
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 eight retained SEEG feature records and source retrieval code. The copied feature package supplies all input trials, pair labels, exclusions, and native headers for this new detector test.
usesdoi:10.18112/openneuro.ds004473.v1.0.2
This version-pinned dataset release supplied the eight SEEG feature records used in the analysis; its saved metadata identifies the release and CC0 terms.
doi:10.1088/1741-2552%2Facae0a

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Public catalog entries on Sciscape.

usesMean replacement reduced SEEG Brier error with simulated white contact noise at one RMS in eight records
Used its directed shaft loss result to identify the need for a spectral scope test. We test high-gamma monitoring under white noise and noise restricted to 0-40 Hz. Its detector outputs were not copied.
usesA simulated 6 dB shaft voltage loss raised mean accepted label error by 10.5 points in eight SEEG readers with fixed early confidence gates
Its single-shaft constant voltage loss stress test motivated the assumed input change. We tested the effect on a fixed confidence gate. We did not reuse its detector code or scores.