Twu31/Qwen3.6-35B-A3B-GPTQ-INT4-W4A16-LowLatency
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updated a model about 14 hours ago
Twu31/Qwen3.6-35B-A3B-GPTQ-INT4-W4A16-LowLatency repliedto their post about 17 hours ago
Can a Jev-style decision interface work on EEG when the questions are asked in words? In our tests, only for questions the model was trained on.
A small head over frozen EEG features (encode a window once, answer several typed questions about it) was asked each question by a question number, a label template or a description. On SSVEP (BETA, 70 people), asking a seen question with a label template instead of its number cost −5.96 pp of balanced accuracy on a plain spectrum (interval −6.84 to −5.08 pp), the 2 pp margin not met. On flicker frequencies the head was never trained on, it reached 28.7%, where training-free CCA reached 80.9% (chance 12.5%).
Results and limits: https://bci.report/topics/questions-in-language/
What "Jev-style" means here: https://bci.report/jev-style/
Query the numbers over MCP: https://huggingface.co/spaces/Twu31/bci-report-explorer
Related write-up: https://huggingface.co/blog/Twu31/one-eeg-encoder-several-questions updated a Space 1 day ago
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Thanks for the comment! Yes, those are the plain-spectrum numbers. On frozen CBraMod features the same three arms all sit around 18–21%, so I think your ceiling point holds there too.
We didn't try the reference signal as the query. The numeric code is the closest thing we have, and it only encodes the fundamental, no harmonics.
If I had to bet, I'd say it's mostly the head: even with one-hot questions it only gets 32.1% on the seen 32-way, while CCA gets 65.5%. Honestly not sure though, and your idea is a really clean way to tell the two apart, so we'll add the reference-spectrum arm and test it. I'll post back here when we have results.
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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
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
published an article 3 days ago
Article
CCA and frozen EEG foundation models on BETA
Twu31
• posted an update 4 days ago
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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: Twu31/bci-report-explorer
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: Twu31/bci-report-explorer
published an article 4 days ago
Article
Choosing how to adapt an EEG foundation model
Twu31
• posted an update 5 days ago
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82
Can a Jev-style decision interface work on EEG when the questions are asked in words? In our tests, only for questions the model was trained on.
A small head over frozen EEG features (encode a window once, answer several typed questions about it) was asked each question by a question number, a label template or a description. On SSVEP (BETA, 70 people), asking a seen question with a label template instead of its number cost −5.96 pp of balanced accuracy on a plain spectrum (interval −6.84 to −5.08 pp), the 2 pp margin not met. On flicker frequencies the head was never trained on, it reached 28.7%, where training-free CCA reached 80.9% (chance 12.5%).
Results and limits: https://bci.report/topics/questions-in-language/
What "Jev-style" means here: https://bci.report/jev-style/
Query the numbers over MCP: Twu31/bci-report-explorer
Related write-up: https://huggingface.co/blog/Twu31/one-eeg-encoder-several-questions
A small head over frozen EEG features (encode a window once, answer several typed questions about it) was asked each question by a question number, a label template or a description. On SSVEP (BETA, 70 people), asking a seen question with a label template instead of its number cost −5.96 pp of balanced accuracy on a plain spectrum (interval −6.84 to −5.08 pp), the 2 pp margin not met. On flicker frequencies the head was never trained on, it reached 28.7%, where training-free CCA reached 80.9% (chance 12.5%).
Results and limits: https://bci.report/topics/questions-in-language/
What "Jev-style" means here: https://bci.report/jev-style/
Query the numbers over MCP: Twu31/bci-report-explorer
Related write-up: https://huggingface.co/blog/Twu31/one-eeg-encoder-several-questions
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published an article 6 days ago
Article
Can one EEG encoder answer several questions?
Twu31
• posted an update 6 days ago
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New write-up: when should an EEG decoder abstain?
Motivated by Jev-style typed decision interfaces; independent evaluation, not affiliated with TypeSafe, AutoTrust, ggml-org or the authors of any Jev paper.
On BETA (70 people, 40-target SSVEP, cue-paced lab trials), one calibrated threshold — accept when the top-class probability is at least 0.8 — accepted very different shares of trials: standard CCA 28.4% (95% interval 22.9–34.2%), EEGNet 16.6% (12.5–21.0%), frozen CBraMod 1.5% (0.8–2.3%), with nothing accepted at all for 38 of the 70 people under CBraMod. A learned reject option did not beat the model's own calibrated confidence, and a certified error level did not reliably hold on new people. These compare methods; they are not deployment error rates.
Article: https://huggingface.co/blog/Twu31/when-should-an-eeg-decoder-abstain
Browse or query the published figures (UI + MCP): Twu31/bci-report-explorer
Collection: Twu31/bci-report-6ac6fc80f1026c08d6cac752
BCI Report is a personal, noncommercial project; aggregate results only.
Motivated by Jev-style typed decision interfaces; independent evaluation, not affiliated with TypeSafe, AutoTrust, ggml-org or the authors of any Jev paper.
On BETA (70 people, 40-target SSVEP, cue-paced lab trials), one calibrated threshold — accept when the top-class probability is at least 0.8 — accepted very different shares of trials: standard CCA 28.4% (95% interval 22.9–34.2%), EEGNet 16.6% (12.5–21.0%), frozen CBraMod 1.5% (0.8–2.3%), with nothing accepted at all for 38 of the 70 people under CBraMod. A learned reject option did not beat the model's own calibrated confidence, and a certified error level did not reliably hold on new people. These compare methods; they are not deployment error rates.
Article: https://huggingface.co/blog/Twu31/when-should-an-eeg-decoder-abstain
Browse or query the published figures (UI + MCP): Twu31/bci-report-explorer
Collection: Twu31/bci-report-6ac6fc80f1026c08d6cac752
BCI Report is a personal, noncommercial project; aggregate results only.
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From Jev-style decisions to EEG: when should a decoder abstain?
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