Cognitive impairments in attention are prevalent among patients with Alzheimer’s disease (AD), Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and Parkinson’s disease dementia (PDD). Electroencephalograms (EEGs), particularly event-related potentials (ERPs), provide valuable insights into these impairments. This study explores the use of Hjorth descriptors—activity, mobility, and complexity—from ERP signals to automatically classify these cognitive disorders. We analyzed EEG data from five subject groups (DLB, PD, PDD, AD, and healthy controls) using k-nearest neighbors, random forest, and gradient boosting classifiers. Data were collected from 90 subjects and each participant underwent neuropsychological assessments and EEG recordings during auditory oddball-distractor tasks. ERP segments were made based on three different events used during the paradigm. Our findings revealed significant differences in the Hjorth descriptors when compared with healthy controls, particularly in the PDD group, where decreased mobility indicated lower mental activity or alertness. The Random Forest classifier outperformed other methods, emphasizing its potential for effective differentiation of cognitive disorders. This study highlights the utility of EEG and machine learning in the early detection and classification of neurodegenerative diseases, offering valuable insights for better patient management.