Geometry-Aware Conformal Decoding for Motor BCI Systems

Roberto Bonini, Zhiyuan Luo, Matteo Filippini, Patrizia Fattori
Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 329:1066-1069, 2026.

Abstract

Brain-computer interface (BCI) motor decoders need accurate kinematic commands and reliable uncertainty. In closed loop, velocity uncertainty can gate or scale commands or give users feedback. Prior non-invasive BCI work used conformal prediction to defer uncertain discrete exoskeleton commands (Eliades and Papadopoulos, 2019); invasive work remains limited to one fixed-width global interval around 1D reach direction or position (Wei et al., 2024). We instead compare complete decoder-geometry systems, not geometries around a common predictor, for continuous 2D velocity decoding.

Cite this Paper

BibTeX
@InProceedings{pmlr-v329-bonini26a, title = {Geometry-Aware Conformal Decoding for Motor BCI Systems}, author = {Bonini, Roberto and Luo, Zhiyuan and Filippini, Matteo and Fattori, Patrizia}, booktitle = {Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {1066--1069}, year = {2026}, editor = {Ahlberg, Ernst and Johansson, Ulf and Boström, Henrik and Carlevaro, Alberto and Hallberg Szabadváry, Johan and Carlsson, Lars}, volume = {329}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v329/main/assets/bonini26a/bonini26a.pdf}, url = {https://proceedings.mlr.press/v329/bonini26a.html}, abstract = {Brain-computer interface (BCI) motor decoders need accurate kinematic commands and reliable uncertainty. In closed loop, velocity uncertainty can gate or scale commands or give users feedback. Prior non-invasive BCI work used conformal prediction to defer uncertain discrete exoskeleton commands (Eliades and Papadopoulos, 2019); invasive work remains limited to one fixed-width global interval around 1D reach direction or position (Wei et al., 2024). We instead compare complete decoder-geometry systems, not geometries around a common predictor, for continuous 2D velocity decoding.} }
Endnote
%0 Conference Paper %T Geometry-Aware Conformal Decoding for Motor BCI Systems %A Roberto Bonini %A Zhiyuan Luo %A Matteo Filippini %A Patrizia Fattori %B Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2026 %E Ernst Ahlberg %E Ulf Johansson %E Henrik Boström %E Alberto Carlevaro %E Johan Hallberg Szabadváry %E Lars Carlsson %F pmlr-v329-bonini26a %I PMLR %P 1066--1069 %U https://proceedings.mlr.press/v329/bonini26a.html %V 329 %X Brain-computer interface (BCI) motor decoders need accurate kinematic commands and reliable uncertainty. In closed loop, velocity uncertainty can gate or scale commands or give users feedback. Prior non-invasive BCI work used conformal prediction to defer uncertain discrete exoskeleton commands (Eliades and Papadopoulos, 2019); invasive work remains limited to one fixed-width global interval around 1D reach direction or position (Wei et al., 2024). We instead compare complete decoder-geometry systems, not geometries around a common predictor, for continuous 2D velocity decoding.
APA
Bonini, R., Luo, Z., Filippini, M. & Fattori, P.. (2026). Geometry-Aware Conformal Decoding for Motor BCI Systems. Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 329:1066-1069 Available from https://proceedings.mlr.press/v329/bonini26a.html.

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