Detection of amyotrophic lateral sclerosis disease from event-related potentials using variational mode decomposition method. Issue 8 (11th June 2022)

• Record Type: Journal Article
• Authors: Orhanbulucu, Fırat; Latifoğlu, Fatma
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 25:Issue 8(2022)
• Page Start: 840
• Page End: 851
• Publication Date: 2022-06-11
• Subjects: Amyotrophic lateral sclerosis -- event-related potentials -- variational mode decomposition -- brain-computer interface -- classification; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2021.1983803
• Languages: English
• ISSNs: 1025-5842

Abstract

ABSTRACT: This study, it was aimed to contribute to the literature on Amyotrophic lateral sclerosis (ALS) diagnosis and Brain-Computer Interface (BCI) technologies by analyzing the electroencephalography (EEG) signals obtained as a result of visual stimuli and attention from ALS patients and healthy controls. It was observed that the success rate significantly increased both in the occipital and central regions in all classifiers, especially in the entropy features. The most successful classification was obtained with the Naïve Bayes (NB) classifier using the Morphological Features (MF) + Variational Mode Decomposition (VMD) -Entropy features at 88.89% in the occipital region and 94.44% in the central region.