One 48-s calibration pair supports above-chance within-patient decoding of amygdala SEEG video blocks
In nine patients, a within-patient shrinkage LDA using six amygdala log band-power features decoded aversive (fearful-face) versus neutral (landscape) video blocks with mean held-out pair accuracy 0.77 after one 48-s calibration pair (Holm-adjusted p = 0.027). The group curve reached 90% of its seven-pair gain at two pairs (96 s); individual k90 values ranged from one to six pairs in eight patients, while one patient remained near chance. A decoder trained on the other eight patients had accuracy 0.61 (block AUC 0.67), including inverted accuracy for one patient. Adding other-patient data with a fixed 0.5 pooling weight did not significantly improve within-patient accuracy at any calibration length. Within-patient accuracy exceeded cross-patient accuracy numerically from two pairs, but this difference was not significant after Holm correction (smallest adjusted p = 0.11). The study includes nine patients and eight block pairs per patient. Block order was fixed, face versus landscape content was not separated from emotion, and label-free z-scoring used the full session, so the pipeline was not strictly causal online. Calibration subsets overlapped, the pooling weight was not tuned, and results may vary across patients.
amygdala · intracranial EEG · SEEG · calibration · shrinkage LDA · band power · within-patient decoding · aversive video
References
3- usesAcross nine amygdala SEEG patients, cross-patient decoding partly identified aversive blocks and underperformed within-patient decoding sci:sha256:48d661462a87fc866b2e76e1ae615babd76779c6916be1ab60ffccbebd80c602
Uses the amygdala band-power features (features.npz), the LDA settings and the upstream feature code of this publication. Measures the per-patient calibration length that it recommended.
- usesdoi:10.18112/openneuro.ds003374.v1.1.1
The analyzed source dataset is recorded as OpenNeuro ds003374 snapshot 1.1.1 (task-jokeit); the feature file was copied from the cited prior SCI contribution.
- doi:10.1016/j.neuroimage.2020.116705
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1 publicationPublic catalog entries on Sciscape.
- supportsIn 9 patients, running-mean amygdala SEEG decoding reached median AUC 0.84 at 10 s, but 4 patients were not stable within 23 s
It found that one patient stayed near chance and 2 patients needed more calibration. We also found patient differences: in our data, 2 patients were stable within 1 s, 3 needed 4.5-13.5 s, and 4 were not stable within 23 s. Both results show that decoder performance in this dataset is very different between patients.