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Updated: Sep 30, 2026

Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment
Published on: May 10, 2024
From confidence to caution: conformal gating for cross-session motor imagery brain-computer interfaces
Sixue Xing1, Aarthy Nagarajan2
1Department of Computer Science and Engineering, University of Notre Dame, 148 Fitzpatrick Hall, University of Notre Dame, IN 46556, Notre Dame, Indiana, 46556, United States.
Objective:
Erroneous motor imagery brain-computer 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.
Approach:
We compare threshold-based CP (THR), regularised adaptive prediction sets (RAPS/RAPS-Opt), and Chow's 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, F0.5, and a risk-weighted utility U(λ).
Main Results:
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 9-16% on OpenBMI and 6-8% on BCIC IV-2a (OpenBMI: all pHolm < 10-9, BCIC IV-2a: pHolm = 0.012). 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 (pHolm < 0.001). Calibtrain, requiring no labelled target-session data, achieved the highest THR F0.5 among cross-session strategies (0.53). Chow issued more commands but incurred higher wrong-decisive rates. Under U(λ), the preferred policy shifted from Chow at λ = 1 to THR for λ ≥ 2; a sensitivity analysis re-optimising Chow's threshold directly for U(λ) did not alter this qualitative ordering (THR above Chow-U for all λ ≥ 2, dz = 0.70-0.93).
Significance:
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.
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