This paper introduces a software framework developed for analyzing EEG signal using machine learning methods. The framework consists of several independent and customizable modules for signal acquisition and preprocessing, feature extraction, model training, evaluation, and interpretation. A unique aspect is the flexibility to tune hyperparameters across all stages of preprocessing and feature extraction. The framework was applied to two tasks: diagnosis of mental disorders and detection of intention to perform a hand movement. The results demonstrate balanced accuracy rates of 91% for schizophrenia diagnosis, 88% for obsessive-compulsive disorder diagnosis and 77% for movement intention detection. The methodologies employed for both tasks are detailed in the study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Original languageEnglish
Title of host publication Intelligent Systems
Pages41-52
Number of pages12
DOIs
StatePublished - 2026
Event16th International Conference on Intelligent Systems - Москва, Russian Federation
Duration: 2 Dec 20244 Dec 2024

Publication series

NameCommunications in Computer and Information Science
Volume2605 CCIS

Conference

Conference16th International Conference on Intelligent Systems
Abbreviated title INTELS 2024
Country/TerritoryRussian Federation
CityМосква
Period2/12/244/12/24

    Research areas

  • electroencephalogram, event-related potentials, machine learning, psychiatric diagnosis, software framework, Biomedical signal processing, Computer aided diagnosis, Diseases, Extraction, Feature extraction, Learning systems, Signal analysis, Customizable, EEG signals, EEG signals analysis, Event related potentials, Features extraction, Machine learning methods, Machine-learning, Psychiatric diagnose, Signal acquisitions, Software frameworks, Electroencephalography

ID: 151442974