BCIwiki (bciwiki.com) — Researchers in the Department of Computer Science and Engineering at the University of Notre Dame have added a gate to motor imagery brain-computer interfaces (BCIs) that holds back commands the system is unsure of, sharply reducing wrong commands when EEG signals drift between sessions. Reporting in the Journal of Neural Engineering on September 28, 2026, they said gating with conformal prediction cut the share of wrong commands from 37.5% to 9–16% on the OpenBMI dataset (54 subjects, 2 classes) and from 35.5% to 6–8% on BCI Competition IV Dataset 2a (9 subjects, 4 classes).
Standard classifiers always output a prediction and have no way to withhold uncertain ones, the authors noted, while EEG signals shift between sessions, so erroneous commands can trigger unintended device actions. A conformal gate issues a command only when the prediction is sufficiently certain.
Using DeepConvNet as the base classifier, the team compared threshold-based conformal prediction (THR), regularized adaptive prediction sets (RAPS and RAPS-Opt) and Chow's selective prediction across 5 chronological split conditions. On OpenBMI, closer matching between calibration and test sessions made the gates more conservative: for THR, the decision rate fell from 0.55 to 0.41 and the wrong-decisive rate from 0.16 to 0.11. A strategy called Calibtrain, which needs no labeled data from the target session, achieved the highest THR F0.5 among cross-session strategies at 0.53. Chow's method issued more commands but made more errors.
Under a risk-weighted utility U(λ), where λ sets how much worse a wrong command is than a missed one, Chow was preferred at λ = 1 and THR at λ ≥ 2, an ordering that held when Chow's threshold was re-optimized for U(λ) (dz = 0.70-0.93). The authors frame cross-session reliability as an application-dependent trade-off between safety and decisiveness. The results come from offline public datasets and did not involve real-time device control or patients.