Learned-Weight Ensemble Monte Carlo DropBlock for Uncertainty Estimation and EEG Classification

Reliable predictive uncertainty—in particular predictive entropy used at decision time—in motor imagery electroencephalography (EEG)-based brain–computer interfaces (BCIs) is critical for safe real-world operation. Although Monte Carlo dropout and deep ensembles are effective fo...

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Detaylı Bibliyografya
Asıl Yazarlar:	Pierre Sedi Nzakuna, Vincenzo Gallo, Marco Carratu, Vincenzo Paciello, Antonio Pietrosanto, Aime Lay-Ekuakille
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	IEEE 2025-01-01
Seri Bilgileri:	IEEE Open Journal of Instrumentation and Measurement
Konular:	Brain-computer interface (BCI) deep learning (DL) digital signal processing (DSP) electroencephalography (EEG) Monte Carlo (MC) methods uncertainty quantification (UQ)
Online Erişim:	https://ieeexplore.ieee.org/document/11271584/
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Learned-Weight Ensemble Monte Carlo DropBlock for Uncertainty Estimation and EEG Classification

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