Haufe activation patterns repeated better than LDA weights across early and late SEEG key-press trials in 6 of 8 subjects
We used the archived per-trial features of sci 04683a2a (OpenNeuro ds004473, 8 SEEG subjects, cued key press). We fit a shrinkage LDA (move vs rest) on trials 1-100 and a second LDA on trials 101 to the end. For each block we calculated the raw weights, the Haufe activation pattern (Cov(X) w) and a Welch t map. Then we calculated the Pearson r between the two blocks over the 100-112 bipolar pairs. The plan was written before the analysis. For 70-150 Hz features, the pattern repeated better than the weights (median r 0.67 vs 0.51; Fisher-z difference +0.44; 6 of 8 subjects; exact sign-flip p = 0.023). With 70-150 Hz + 13-30 Hz features the effect was larger (+0.57, 7 of 8, p = 0.008). Equal block sizes gave the same result. The top-10% channels by |pattern| overlapped more between blocks than the top-10% by |weight| (Jaccard +0.19, p = 0.047). A simple t map repeated as well as the pattern. Map stability followed decoder quality (Spearman rho 0.86-0.93 with transfer AUC). In the subject whose decoder was at chance (AUC 0.50), no map repeated. Thus, to find SEEG channels that carry key-press signal, use the activation pattern or t map, not the raw LDA weights. Limits: 8 subjects, one session, offline analysis, one decoder type; an odd/even split of trials 1-100 did not show a significant difference.
SEEG · brain-computer interface · decoder interpretation · Haufe activation pattern · linear discriminant analysis · map stability · intracranial EEG · key-press detection
References
2- usesBeta power increased later-trial SEEG key-press AUC in 6 of 8 subjects sci:sha256:b76113878b22b13b8fce6b4b57cf321bb22ce16b618b55ae02c2ef42b972f816
We used the archived per-trial 70-150 Hz and 13-30 Hz features and the LDA code of this package without change.
- usesdoi:10.18112/openneuro.ds004473.v1.0.2
The archived per-trial features used in this analysis were derived from the SlowFast task in OpenNeuro ds004473 version 1.0.2; the recorded dataset metadata identifies this version and its CC0 license.
Cited by
2 publicationsPublic catalog entries on Sciscape.
- supportsHaufe patterns repeat better than raw decoder weights across list halves in 18 SEEG patients
On a different task and data set (FR1 word recall, ds004789, 18 patients, 704-1384 features), the Haufe pattern again repeated between two blocks of one session better than the raw LDA weights (17/18 patients, exact sign-flip p = 7.6e-6). A post-hoc check that scaled each r by its own label-shuffle null also showed the effect (15/18, p = 0.0025).
- usesHaufe patterns repeat better than raw decoder weights across list halves in 18 SEEG patients
The analysis uses the map-stability method and the unchanged numpy shrinkage-LDA code (lda_np.py) of this publication.
- Haufe activation patterns repeated better than raw LDA weights across trial blocks in one-session SEEG data from 15 patients
[Legacy relation: reproduces] Same map-repeat test (Haufe pattern vs raw LDA weight) in an independent dataset, task and center (ds006253, 15 patients); the result replicated (14/15, p = 6.1e-5).