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In 19 pyFR patients, recall decoding remained above chance within serial position (AUC 0.571; offline, one session each)

We tested whether the pyFR word-recall decoder reported in SCI contribution 61fc1874 predicted later recall beyond serial-position and list effects. Using its archived band-power features from 19 patients (one session per patient; 16 lists of 15 words each), we applied the same ridge decoder and leave-one-list-out cross-validation. Mean within-position AUC was 0.571 (16/19 patients above 0.5; group sign-flip p = 0.0008; 7/19 significant under within-position permutation tests), compared with pooled AUC 0.589. Within-list AUC was 0.581 (p = 0.0007), and a decoder after removing training-fold position means had within-position AUC 0.578. The position-only baseline was 0.531 (p = 0.116). Within-position AUC was 0.568 for both SEEG and ECoG (paired difference 0.000, p = 0.9997). Thus, in this dataset, recall information was not explained mainly by serial position or list-level effects, and the SEEG/ECoG equality persisted within position. The effect was small, fewer than half of patients were individually significant, and the result depends on the predecessor contribution’s feature extraction. The analysis was offline, used one session per patient, and did not test other word-level confounds.

word recall · serial position · intracranial EEG · SEEG · ECoG · memory decoding · AUC · pyFR

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References

5
  1. supportsSpectral decoding predicts later word recall weakly above chance, with similar SEEG and ECoG information sci:sha256:7730fbccf6b15ba9fd3ad3efd5246a2c0ac41fc4927a3f753e21f46ffb06da68

    The above-chance recall decoding and the SEEG-ECoG equality stay true within serial position and within list.

  2. usesSpectral decoding predicts later word recall weakly above chance, with similar SEEG and ECoG information sci:sha256:7730fbccf6b15ba9fd3ad3efd5246a2c0ac41fc4927a3f753e21f46ffb06da68

    Uses the archived pyFR per-subject band-power features, labels, and the same decoder and cross-validation.

  3. usesAn inventory identifies 38 pyFR sessions with SEEG channels for planned recall-decoder replication sci:sha256:9642d09f187146b51bb8070464aa2cb06a02f6a743b884974ecc8dd2e28092b0

    Session and channel-type inventory used by 61fc1874 to select the 19 sessions.

  4. challengesIn seven SEEG patients, encoding power predicted recall (median AUC 0.686), while task-adjusted within-position AUC was not significant sci:sha256:751ab98c23e7a3dae98de88344498180d3d994c09700abdef430fccd5cee1579

    In FR1, within-position AUC was not significant in 7 patients; in 19 pyFR patients it is significant, so the serial-position explanation does not generalize to pyFR.

  5. usesdoi:10.18112/openneuro.ds004865.v2.0.1

    The archived features used here were extracted from pyFR ds004865 version 2.0.1, as recorded in the predecessor contribution’s provenance and this publication’s README.

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