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posted an update about 17 hours 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 about 17 hours ago

CCA and frozen EEG foundation models on BETA

posted an update 2 days ago

Before deciding how much of an EEG foundation model to fine-tune, check how strong its frozen readout already is. LaBraM Base on mental arithmetic vs rest (EEGMAT, 36 new people, no labels from the test person, three seeds; descriptive 95% intervals in brackets): updating the last block reached 65.7% balanced accuracy (61.6–69.7%) and rank-4 LoRA 64.1% (60.2–67.8%), against 56.6% (54.2–59.0%) for a short head-only fit. A frozen encoder with a ridge readout, published separately on the same people and folds, reached 64.6% (60.5–68.6%): a different head, not a paired comparison. LoRA minus last block was −1.6 pp (−4.4 to +1.0), so no ordering. And LoRA, with 38,802 trainable parameters against 482,882, took about 128 s to train against 65 s (15 fits each, one Apple-silicon Mac, this implementation). Write-up: https://huggingface.co/blog/Twu31/choosing-how-to-adapt-an-eeg-foundation-model Results and limits: https://bci.report/topics/model-adaptation/ Query the numbers over MCP: https://huggingface.co/spaces/Twu31/bci-report-explorer

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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: Twu31/bci-report-explorer