Riemannian classification of EEG signals with missing values
- URL: http://arxiv.org/abs/2110.10011v1
- Date: Tue, 19 Oct 2021 14:24:50 GMT
- Title: Riemannian classification of EEG signals with missing values
- Authors: Alexandre Hippert-Ferrer, Ammar Mian, Florent Bouchard and
Fr\'ed\'eric Pascal
- Abstract summary: This paper proposes two strategies to handle missing data for the classification of electroencephalograms.
The first approach estimates the covariance from imputed data with the $k$-nearest neighbors algorithm; the second relies on the observed data by leveraging the observed-data likelihood within an expectation-maximization algorithm.
As results show, the proposed strategies perform better than the classification based on observed data and allow to keep a high accuracy even when the missing data ratio increases.
- Score: 67.90148548467762
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper proposes two strategies to handle missing data for the
classification of electroencephalograms using covariance matrices. The first
approach estimates the covariance from imputed data with the $k$-nearest
neighbors algorithm; the second relies on the observed data by leveraging the
observed-data likelihood within an expectation-maximization algorithm. Both
approaches are combined with the minimum distance to Riemannian mean classifier
and applied to a classification task of event related-potentials, a widely
known paradigm of brain-computer interface paradigms. As results show, the
proposed strategies perform better than the classification based on observed
data and allow to keep a high accuracy even when the missing data ratio
increases.
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