Orhanbulucu, FiratLatifoglu, Fatma2024-08-042024-08-0420221025-58421476-8259https://doi.org/10.1080/10255842.2021.1983803https://hdl.handle.net/11616/100241This 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 Naive 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.eninfo:eu-repo/semantics/closedAccessAmyotrophic lateral sclerosisevent-related potentialsvariational mode decompositionbrain-computer interfaceclassificationArticle2588408513460200110.1080/10255842.2021.19838032-s2.0-85116397131Q3WOS:000703441700001Q4