Uncertainty Quantification for cross-subject Motor Imagery classification

Autor:	Manivannan, Prithviraj, de Jong, Ivo Pascal, Valdenegro-Toro, Matias, Sburlea, Andreea Ioana
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Uncertainty Quantification aims to determine when the prediction from a Machine Learning model is likely to be wrong. Computer Vision research has explored methods for determining epistemic uncertainty (also known as model uncertainty), which should correspond with generalisation error. These methods theoretically allow to predict misclassifications due to inter-subject variability. We applied a variety of Uncertainty Quantification methods to predict misclassifications for a Motor Imagery Brain Computer Interface. Deep Ensembles performed best, both in terms of classification performance and cross-subject Uncertainty Quantification performance. However, we found that standard CNNs with Softmax output performed better than some of the more advanced methods.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.09228 Zobrazit plný text záznamu View this record from Arxiv