Interpretable EEG biomarkers for neurological disease models in mice using bag-of-waves classifiers
Key Points
This research aims to identify interpretable EEG biomarkers to differentiate epilepsy genotypes in mice.
Utilized EEG waveforms as potential biomarkers for epilepsy.
Applied bag-of-waves classifiers for feature representation.
Conducted analysis on mouse models of neurological diseases.
EEG waveforms effectively served as interpretable phenotypes.
Bag-of-waves classifiers successfully identified different epilepsy genotypes.
Abstract
The methodologies and results show the potential of EEG waveforms as interpretable phenotypes and bag-of-waves as a feature representation for identifying epilepsy genotypes.