BCI ReportDecision research · three routes
Jev-style questions on EEG: the evidence
Three routes, each answered by BCI Report’s own runs on public EEG data.
What the name means
Jev is TypeSafe AI’s decision model ↗. Software sends it the state of an application and typed questions; it returns typed decisions, such as a choice from a fixed set, a score or a yes or no, each with a probability or a confidence, instead of generating text. “Jev-style” here means the interface of a decision model like TypeSafe’s Jev, applied to EEG: encode the recording once, then answer several explicit, typed questions about it, each with a probability.
This site’s decision-research plan started from a vision paper, Yu & Yao, 2026 · Visual Jev: Accurate and Efficient Decisions from Shared Visual Context ↗, which applies the same encode-once, answer-many idea to images: one encoding of an image and its context serves several independent forced-choice questions. The three routes below ask whether that holds up on EEG, with this site’s own runs on public datasets.
Three routes
What each route found
Each answer is in words; the figures, their intervals and their limits are on each route’s own page.
First route
Reliable decisions
When should a decoder decline to decide? Rescoring the saved outputs of models this site already publishes: compare methods at matched coverage, not at a shared cut-off. At the same coverage, a learned reject option did not err less than calibrated confidence for any method, and a certified risk accepted nothing on one of the two protocols.
Second route
One representation, several questions
Can one EEG model answer several questions as well as separate models? Fixed outputs on one shared encoder were cheaper than a head told which question it answers, but were not shown to do as well; telling the head the question, by an identifier, added nothing measurable.
Third route
Questions in language
Can an EEG model answer questions asked in words? Only for questions it was trained on, and not on every dataset: in its training wording, a small head matched question numbers on seen sleep questions but lost accuracy on SSVEP; most rewordings of a seen question cost accuracy; and questions it was never trained on were not answered usefully. In a boundary probe, negated questions were answered as if they asked for what they negate.
The plan, its literature and what each route set out to test: the research plan on When not to act →
A longer write-up of the first route’s BETA results is on Hugging Face: When should an EEG decoder abstain? ↗
BCI Report is an independent project; it does not use or evaluate TypeSafe’s Jev or the Visual Jev model.