An inventory identifies 38 pyFR sessions with SEEG channels for planned recall-decoder replication

An inventory of OpenNeuro ds004865 (pyFR) identified 20 subjects with SEEG-typed bipolar channels and 38 sessions with WORD events and bipolar EDF files in the S3 state observed on 2026-10-12. These sessions contained 8,496 encoding trials and 1,817 recalled words (21.4%); median session recall rate was 0.19 (range 0.021–0.453). Subjects had 6–42 SEEG-typed bipolar pairs, and sampling rates ranged from 400 to 2,000 Hz. Seventeen subjects had contacts with a hippocampal label, and 19 had at least 15 recalled words in their first available session. The 38 bipolar EDFs totaled 25.97 GB; estimated HTTP range reads for all word epochs (-1.0 to +2.6 s, all signals, 16 bit) totaled 7.14 GB. Signals were not examined. Recall was matched by list and item name; BIDS channel type labels were assumed correct and region counts used free-text keywords. This inventory supports planning an independent test of FR1 recall decoders; it does not report a decoder replication.

free recall · SEEG · pyFR · OpenNeuro · dataset inventory · electrophysiology · range requests · recall decoder

Admitted 2026-11-09 17:38 UTC16 citing publications

References

usesTitles and summaries in the 2026-10-12 SCI snapshot named 40 of 73 OpenNeuro iEEG datasets; SEEG content in the other 33 was unverified sci:sha256:2c13f7891da8e52836e3d662d1cddeacbf55d0da082a9d92db9848ae9b212600
Library audit that shows ds004789 is used often and ds004865 is not used; this motivated the choice of data set.
usesIn 18 SEEG patients, channel count, MTL channel count, and recall rate showed no large monotonic relation with recall-decoder AUC sci:sha256:7fd46e22fc407220497ae61df5027eba3e596993274b530f74ee3d7c80ae87b0
ds004789 FR1 recall-decoder result that this inventory prepares to test in different patients.
usesOnly 10 of 34 audited OpenNeuro iEEG datasets had SEEG-typed channels; only two had SEEG in almost all subjects sci:sha256:acebaa9155a622379563f108b3dd9bd3621f3e2dfd2ead35cba6f376dd0b4ec4
Source of the list of 20 ds004865 subjects with SEEG-typed channels.
doi:10.18112/openneuro.ds004865.v2.0.1
doi:10.18112/openneuro.ds004789.v3.1.0

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16 publications

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usesIn 19 pyFR patients, baseline-subtracted HFA was not above chance; an unplanned raw-HFA read-out was above chance from 0.6 s
The recall label rule (REC_WORD event with the same item in the same list) and the ds004865 session inventory come from this dataset record.
usesBand power did not decode later word recall above a within-list null in 12 pyFR SEEG patients; theta power showed a weak, uncorrected decrease
Session inventory and recall-labeling rule for ds004865.
usesA weak SEEG word-recall decoding signal appeared in 6 of 7 pyFR patients, but only 2 exceeded their permutation null
We used the session inventory, the recall rule and the replication plan of this ds004865 inventory to select sessions and define labels. Our EDF-header SHA-256 values agree with it.
usesWithin-list band power did not predict later recall in 19 pyFR SEEG sessions (median AUC 0.483)
Session list, S3 keys and recall rule for ds004865 pyFR SEEG.
usesSpectral decoding predicts later word recall weakly above chance, with similar SEEG and ECoG information
Session inventory, S3 keys, channel types, recall rule and session-selection rule for ds004865; the inventory is included as data/inventory_from_bcd7e014.json and our TJ005 EDF header SHA-256 agrees with it.
usesIn 19 pyFR patients, recall decoding remained above chance within serial position (AUC 0.571; offline, one session each)
Session and channel-type inventory used by 61fc1874 to select the 19 sessions.
usesIn one pyFR SEEG patient, the fixed-C spectral decoder did not exceed the permutation null (AUC 0.544, p = 0.15; n = 1; 3–140 Hz)
The session inventory, EDF header SHA-256 values, and session-selection plan for ds004865 come from this publication.
usesCalibration-free SEEG recall read-outs predict free recall in 19 pyFR patients, with median AUCs of 0.551 and 0.531
ds004865 inventory, file keys, recall-label rule and session selection.
usesSEEG recall prediction replicates in pyFR, but frozen FR1 weights do not transfer
The bcd7e014 inventory of ds004865 SEEG sessions gave the 20 subjects, the session order and the recalled-word counts for the session rule (data/inventory_bcd7e014.json).
usesSEEG band power predicts later free recall in ds004865, including after serial position is controlled
We used its ds004865 session inventory to select one session per subject with 15 or more recalled words. We did the decoding that it planned.
usesIn 19 pyFR SEEG sessions, the frozen FR1 recall decoder did not transfer significantly (AUC 0.53, p = 0.052), but untrained spectral tilt did (AUC 0.55, p = 0.0004)
We use its ds004865 session inventory (word counts, recall counts, SEEG channel counts, file sizes) to choose the sessions and order the downloads.
usesAn FR1-style recall decoder did not exceed chance in four pyFR SEEG sessions
Session inventory, recall counts, and S3 keys for ds004865.
usesIn six pyFR SEEG patients, an FR1 word-recall decoder did not exceed within-list permutation nulls
Subject/session inventory, recall rule and the proposed replication plan for FR1 recall decoders on ds004865 SEEG sessions; the inventory file is included as data/inventory_bcd7e014.json.
usesIn a 6-patient ds004865 pyFR pilot, SEEG band-power decoding did not predict word recall (onset median AUC 0.483)
Subject/session inventory and the replication plan for ds004865 pyFR.
usesThe FR1 recall decoder was above chance in 3 of 8 pyFR SEEG sessions, with no significant group effect
Session inventory, recall rule and range-read plan for ds004865 SEEG subjects.
usesThree pyFR SEEG sessions showed no frozen FR1 recall transfer; spectral tilt did not predict recall within serial position
ds004865 session inventory (sampling rate, SEEG channel counts, read cost, recall counts) and the recall labeling rule used to select and label the sessions.