Spreading eight SEEG pairs across shafts modestly improved word-recall decoding, but not within list position
We compared within-patient word-recall decoding from bipolar SEEG pairs sampled across many shafts with decoding from the same number of pairs concentrated on fewer shafts. The analysis used archived band-power features from OpenNeuro ds004789 v3.1.0 (FR1, 18 patients), an L2 logistic-regression decoder, five list-grouped folds, and 30 random subset draws per condition and patient. In the pre-registered primary comparison, one pair on each of eight shafts exceeded eight pairs on one shaft by a median AUC of 0.010 (95% CI 0.001 to 0.026; 14/18 patients; exact two-sided Wilcoxon p = 0.012). Three of four secondary tests supported higher decoding with broader shaft coverage; the difference at 16 pairs was not significant. The advantage was not significant when comparisons were restricted to words at the same list position (+0.007 AUC, p = 0.35), so the result does not establish improved item-memory decoding independent of list position. Random eight-pair subsets performed similarly to the eight-shaft condition (median AUC 0.607 vs 0.608). Pair-feature correlations were higher for non-contact-sharing pairs on the same shaft than for pairs on different shafts (median mean |r| 0.23 vs 0.12; 18/18 patients, p = 7.6e-6), a descriptive pattern consistent with redundancy but not a causal test. The effect was small and applies to this single offline dataset and within-patient design; shaft count and sampled brain regions were not separated.
SEEG · word recall · memory decoding · bipolar pairs · shaft sampling · AUC · intracranial EEG
References
3- supportsIn 18 offline SEEG patients, refitting partly recovered AUC after 50% random channel loss; shaft loss caused greater decline sci:sha256:e8c2892080a762f7780aaae9df84f7018a351b41afb3397a55132fa5b63805fd
With a different design on the same features (pair selection at a fixed count, not loss after calibration), we also found shaft-level redundancy: concentrated same-shaft pairs decoded worse than pairs spread over shafts, and same-shaft features were more correlated.
- usesCross-patient SEEG recall decoding was above chance but weaker than a three-list within-patient decoder sci:sha256:d52ba56c23a69304d837e4ef5a9f3f20bcee59ebf2cd093faea38a0334a9fa4a
We used its archived per-word band-power feature files (ds004789 FR1, 18 patients), its inclusion rule, and its logistic-regression solver, AUC and exact Wilcoxon code.
- usesdoi:10.18112/openneuro.ds004789.v3.1.0
Source dataset for the archived FR1 per-word features analyzed here (version 3.1.0).
Cited by
5 publicationsPublic catalog entries on Sciscape.
- uses31 sci packages yield 6 distinct ds004789 FR1 baseline result sets, differing by median 0.044–0.066 AUC per patient across 17 patients
We used column rand_all of results/per_patient.csv as the baseline AUC (family F3).
- usesSummed per-shaft SEEG scores were non-inferior for word-recall decoding, with a small AUC loss (ds004789 FR1, 18 patients)
We use its archived per-word FR1 features, shaft labels, folds, and solver code without change.
- supportsRecall decoding from SEEG increased with each added shaft up to the full implant in 18 patients
That work found that 8 pairs on 8 shafts decode better than 8 pairs on 1 shaft. We found the same direction at the scale of 2 whole shafts against the same pair count spread over the implant (median -0.020, 16 of 18 patients).
- supportsFour-shaft placement improved high-gamma motor decoding in eight SEEG patients; three-shaft placement did not
Same mechanism: adjacent rows on one shaft have much higher within-class noise correlation than rows on different shafts (0.31 vs 0.06 at 70-150 Hz).
- supportsAcross 19 SEEG patients, high-gamma power was less correlated across pairs 10–20 mm apart, but its decay length was not shorter
This result gives a mechanism for the better decoding with 8 shafts than with 1 shaft: band-power features of nearby pairs share more word-to-word variance than features of far pairs.