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Uncertainty Quantification for Cross-subject Motor Imagery Classification

Year: 2024

Type: conference-paper

Abstract: 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.

Source: University of Groningen research database (University of Groningen / Centre for Information Technology)

DOI: 10.3217/978-3-99161-014-4-016

Authors: Manivannan, Prithviraj, de Jong, Ivo Pascal, Valdenegro-Toro, Matias, Sburlea, Andreea Ioana

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

OpenAlex ID: W6964783595

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