Spread SEEG channel placement improved memory decoding in three computable tests
We tested whether spreading a fixed number of stereo-electroencephalography (SEEG) bipolar pairs across shafts improved memory decoding compared with placing adjacent pairs on one shaft. We analyzed archived features for 19 subjects in OpenNeuro ds004789 (free recall) and 11 subjects in ds004752 (Sternberg working memory). For each eligible subject and channel budget, we compared 100 random dense sets with 100 random spread sets using shrinkage linear discriminant analysis, high-gamma and beta power, and five-fold cross-validation. Spread placement increased mean area under the receiver operating characteristic curve (AUC) in all three computable tests: FR1 K=4, +0.018 (15/19 subjects; Holm-adjusted p=0.0019); FR1 K=8, +0.019 (14/19; p=0.0086); and working memory K=4, +0.029 (10/11; p=0.024). The preregistered working-memory K=8 test could not be computed because no shaft had eight bipolar pairs. Exploratory K=7 gains were +0.019 for FR1 (p=0.0057) and +0.036 for working memory (p=0.0625); these tests were outside the Holm family. The memory-decoding gain at K=8 was about 35% of the reported motor-decoding gain. This analysis tested random placement only, used one decoder and two band-power features per pair, and did not distinguish effects of brain-region coverage from between-shaft noise correlation.
stereo-electroencephalography · SEEG · memory decoding · free recall · working memory · channel placement · bipolar pairs · shrinkage LDA · AUC · high-gamma power · beta power
References
5- usesBeta power increased later-trial SEEG key-press AUC in 6 of 8 subjects sci:sha256:b76113878b22b13b8fce6b4b57cf321bb22ce16b618b55ae02c2ef42b972f816
Numpy shrinkage LDA and AUC code (lda_np.py).
- supportsSpreading SEEG bipolar pairs across shafts improved key press decoding at 4, 8, and 16 pair budgets in eight subjects sci:sha256:76360f9203f1de0c81ac69f3143936f1f395c03aeb4d95c5d5032441cafad99e
The spread-over-dense placement rule for motor decoding also applies to two memory decoding tasks, with a smaller gain.
- usesSEEG load decoding improves with channel count, while training fold selection adds a small and inconsistent gain sci:sha256:a1b1bdd0f518fbb2c2df6602d834b6e285ba787275d9c6aebd64474017c360b0
Archived Sternberg (ds004752) band-power features for 15 subjects.
- usesCross-patient SEEG recall decoding was above chance but weaker than a three-list within-patient decoder sci:sha256:d52ba56c23a69304d837e4ef5a9f3f20bcee59ebf2cd093faea38a0334a9fa4a
Archived FR1 (ds004789) band-power features for 19 subjects.
- usesBeta power improved key-press decoding but showed no significant pooled gain across three memory tasks sci:sha256:3b05dc4cb77a736130c17a219a6a096c43b1da128f51f01c0d99204ecddb4600
Loader, band definitions and trial selection for both feature sets.
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