Twu31/bci-report
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lasfk/EEG-fNIRS-based-Handwriting-Trajectory-Dataset posted an update 1 day ago
Before reaching for an EEG foundation model for an SSVEP speller, compare it with CCA, which needs no training at all.
BETA, 40 targets, 70 new people, eight posterior electrodes (chance 2.5%; descriptive 95% intervals in brackets): standard CCA reached 63.1% balanced accuracy (57.2–69.0%). Frozen LaBraM and CBraMod with ridge heads reached 10.8% (9.5–12.2%) and 33.7% (29.8–37.6%); EEGNet trained from scratch, 55.8% (50.1–61.3%). 16 further foundation-model checkpoints in the same frozen recipe reached at most 54.8% (49.4–60.0%), and none lies above CCA with eight electrodes or with four (57.6%). These are frozen probes with one fixed recipe, not tuned ceilings for any model.
Write-up: https://huggingface.co/blog/Twu31/cca-and-frozen-eeg-foundation-models-on-beta
Protocol, downloads and caveats: https://bci.report/protocols/beta-8ch/
Query the numbers over MCP: https://huggingface.co/spaces/Twu31/bci-report-explorer published an article 1 day ago
CCA and frozen EEG foundation models on BETAOrganizations
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