A fixed SEEG reader had mean recall AUC of 0.547 for random words and 0.544 for complete lists in eight sessions
We tested whether the test unit changes the measured accuracy of a fixed SEEG memory reader. We used 1,920 words from eight session-zero recordings in public OpenNeuro ds004809 version 2.2.0. Each reader used 16 bipolar SEEG pairs and six power bands. Random-word and complete-list tests both trained on 192 words per fold. Across ten repeats, mean recall AUC was 0.547 and 0.544. The mean difference was 0.003. Its descriptive participant bootstrap 95 percent interval was -0.011 to 0.017. The sample does not establish a mean split effect or equivalence. A position-only reader had mean AUC of 0.587 and 0.583. A list-identity control gave tied scores within every complete-list test fold. Its AUC was 0.500 within folds and 0.435 when scores from different folds were pooled. A proof explains this difference under the fixed linear reader assumptions. The package preserves inputs, source versions and hashes, all fitted models, controls, three figures, and code. An independent EDF reader rebuilt all features for one participant. A separate analysis run reproduced the numerical outputs. These are offline tests within a session. They do not establish online BCI performance or a stimulation effect.
stereo-EEG · memory · free recall · AUC · cross-validation · brain-computer interface · score pooling
References
3- usesdoi:10.18112/openneuro.ds004809.v2.2.0
The study used bipolar SEEG voltage, word and recall events, channel tables, and release metadata from OpenNeuro ds004809 v2.2.0.
- doi:10.1038/s41467-017-02753-0
- doi:10.1093/cercor%2Fbhad008
Cited by
5 publicationsPublic catalog entries on Sciscape.
- usesThe mean non-recall rate was 70.1% for a sequential SEEG and task rule with three selections per list in 16 later sessions
The direct parent retained this 16-pair, six-band power feature design and source code. We use those features and independently check all raw extraction windows.
- usesAdded SEEG power increased recall forecast loss in all eight later sessions with a fixed logistic reader
We reused the EDF byte-range reader and the six-band power feature rule. We added paired-session selection and trained new readers.
- usesThe mean non-recall rate was 70.1% for a sequential SEEG and task rule with three selections per list in 16 later sessions
The direct parent retained this 16-pair, six-band power feature design and source code. We use those features and independently check all raw extraction windows.
- usesA local power reference lowered recall forecast loss compared with absolute power in only three of eight later SEEG sessions at C=0.1
Uses the EDF decoder and six-band power rule through the parent study. These methods provide matched pre-word and post-word features.
- usesA fixed SEEG recall reader with scaling from 60 early words had higher mean loss than task inputs in eight new session pairs at C = 0.01
We used the fixed SEEG feature design from this study through the directly cited later package: 16 bipolar pairs, six log power bands, and the one-second window that starts 0.3 s after word onset.