New Applications of Late Fusion Methods for EEG Signal Processing

Decision fusion consists in the combination of the outputs of multiple classifiers into a common decision that is more precise or stable. In most cases, however, only classical fusion techniques are considered. This work compares the performance of several state-of-the-art fusion methods on new appl...

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Bibliographic Details
Published in2019 International Conference on Computational Science and Computational Intelligence (CSCI) pp. 617 - 621
Main Authors Safont, Gonzalo, Salazar, Addisson, Vergara, Luis
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Subjects
alpha integration
classification
EEG
Electroencephalography
Estimation
Feature extraction
Independent component analysis
late fusion
Mixture models
neuropsychological tests
Probability density function
Resource description framework
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Summary:Decision fusion consists in the combination of the outputs of multiple classifiers into a common decision that is more precise or stable. In most cases, however, only classical fusion techniques are considered. This work compares the performance of several state-of-the-art fusion methods on new applications of automatic stage classification of several neuropsychological tests. The tests were staged into three classes: stimulus display, retention interval, and subject response. The considered late fusion methods were: alpha integration; copulas; Dempster-Shafer combination; independent component analysis mixture models; and behavior knowledge space. Late fusion was able to improve the performance for the task, with alpha integration yielding the most stable result.
DOI:10.1109/CSCI49370.2019.00116