Universum based Lagrangian twin bounded support vector machine to classify EEG signals.
Journal:
Computer methods and programs in biomedicine
Published Date:
Jun 24, 2021
Abstract
BACKGROUND AND OBJECTIVE: The detection of brain-related problems and neurological disorders like epilepsy, sleep disorder, and so on is done by using electroencephalogram (EEG) signals which contain noisy signals and outliers. Universum data contains a set of a sample that does not belong to any of the concerned classes and serves as the advanced knowledge about the data distribution. Earlier information has been utilized viably in improving classification performance. Recently a novel universum support vector machine (USVM) was proposed for EEG signal classification and further, a universum twin support vector machine (UTWSVM) was proposed based on USVM to improve the performance. Inspired by USVM and UTWSVM, this paper suggests a novel method called universum based Lagrangian twin bounded support vector machine (ULTBSVM), where universum data is utilized to incorporate the prior information about the data distribution to classify healthy and seizure EEG signals.