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Neural Eng."],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"Abstract<\/jats:title>\n \n Objective.<\/jats:italic>\n Erroneous motor imagery brain\u2013computer interface (MI-BCI) commands can cause unintended device actions, yet standard classifiers provide no mechanism to withhold uncertain predictions. We evaluate whether conformal prediction (CP) can serve as a command gate under cross-session EEG shift, where exchangeability cannot be assumed, and how gating-policy choice should vary with application-specific risk tolerance.\n Approach.<\/jats:italic>\n We compare threshold-based CP (THR), regularised adaptive prediction sets (RAPS\/RAPS-Opt), and Chow\u2019s selective prediction. Experiments use DeepConvNet as the base classifier on OpenBMI (54 subjects, 2-class) and BCI Competition IV Dataset 2a (BCIC IV-2a: 9 subjects, 4-class). Five chronological split conditions evaluate calibration sources with increasing match to the target session. Performance is assessed using decision rate, wrong-decisive rate, empirical coverage,\n \n \n \n <\/jats:tex-math>\n \n \n \n F<\/mml:mi>\n \n 0.5<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , and a risk-weighted utility\n \n \n \n <\/jats:tex-math>\n \n \n U<\/mml:mi>\n (<\/mml:mo>\n \u03bb<\/mml:mi>\n )<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n .\n Main results.<\/jats:italic>\n Ungated cross-session classifiers produced wrong-decisive rates of 37.5% on OpenBMI and 35.5% on BCIC IV-2a. Across THR and RAPS, these rates were reduced to 9%\u201316% on OpenBMI and 6%\u20138% on BCIC IV-2a (OpenBMI: all\n \n \n \n <\/jats:tex-math>\n \n \n \n p<\/mml:mi>\n \n \n Holm<\/mml:mi>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:msub>\n <<\/mml:mo>\n \n 10<\/mml:mn>\n \n \u2212<\/mml:mo>\n 9<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , BCIC IV-2a:\n \n \n \n <\/jats:tex-math>\n \n \n \n p<\/mml:mi>\n \n \n Holm<\/mml:mi>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:msub>\n =<\/mml:mo>\n 0.012<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ). On OpenBMI, increasing calibration-test match made conformal gates more conservative: for THR, decision rate fell from 0.55 to 0.41 and wrong-decisive rate from 0.16 to 0.11 (\n \n \n \n <\/jats:tex-math>\n \n \n \n p<\/mml:mi>\n \n \n Holm<\/mml:mi>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:msub>\n <<\/mml:mo>\n 0.001<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ).\n \n \n \n <\/jats:tex-math>\n \n \n \n \n Calib<\/mml:mi>\n <\/mml:mrow>\n \n \n train<\/mml:mi>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , requiring no labelled target-session data, achieved the highest THR\n \n \n \n <\/jats:tex-math>\n \n \n \n F<\/mml:mi>\n \n 0.5<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n among cross-session strategies (0.53). Chow issued more commands but incurred higher wrong-decisive rates. Under\n \n \n \n <\/jats:tex-math>\n \n \n U<\/mml:mi>\n (<\/mml:mo>\n \u03bb<\/mml:mi>\n )<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , the preferred policy shifted from Chow at\n \n \n \n <\/jats:tex-math>\n \n \n \u03bb<\/mml:mi>\n =<\/mml:mo>\n 1<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n to THR for\n \n \n \n <\/jats:tex-math>\n \n \n \u03bb<\/mml:mi>\n \n \u2a7e<\/mml:mtext>\n <\/mml:mrow>\n 2<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ; a sensitivity analysis re-optimising Chow\u2019s threshold directly for\n \n \n \n <\/jats:tex-math>\n \n \n U<\/mml:mi>\n (<\/mml:mo>\n \u03bb<\/mml:mi>\n )<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n did not alter this qualitative ordering (THR above Chow-\n \n \n \n <\/jats:tex-math>\n \n \n U<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n for all\n \n \n \n <\/jats:tex-math>\n \n \n \u03bb<\/mml:mi>\n \n \u2a7e<\/mml:mtext>\n <\/mml:mrow>\n 2<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ,\n \n \n \n <\/jats:tex-math>\n \n \n \n d<\/mml:mi>\n z<\/mml:mi>\n <\/mml:msub>\n =<\/mml:mo>\n 0.70<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n \u2013\n \n \n \n <\/jats:tex-math>\n \n \n 0.93<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ).\n Significance.<\/jats:italic>\n This study reframes cross-session MI-BCI reliability as an application-dependent safety-decisiveness trade-off, providing a framework for selecting calibration sources and uncertainty-aware gating policies according to wrong-command tolerance under session shift.\n <\/jats:p>","DOI":"10.1088\/1741-2552\/aead26","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T22:52:02Z","timestamp":1790635922000},"page":"056057","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["From confidence to caution: conformal gating for cross-session motor imagery brain\u2013computer interfaces"],"prefix":"10.1088","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0308-2486","authenticated-orcid":true,"given":"Sixue","family":"Xing","sequence":"first","affiliation":[{"name":"University of Notre Dame"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4649-060X","authenticated-orcid":true,"given":"Aarthy","family":"Nagarajan","sequence":"additional","affiliation":[{"name":"University of Notre Dame"},{"name":"University of Notre Dame"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,10,23]]},"reference":[{"key":"jneaead26bib1","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1016\/S1388-2457(02)00057-3","type":"journal-article","article-title":"Brain\u2013computer interfaces for communication and control","volume":"113","author":"Wolpaw","year":"2002","journal-title":"Clin. 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