Synthetic EEG Signal Generator of Morphologies Associated with Epileptogenic Events

Electroencephalography is crucial for understanding brain signals. This work presents a methodology for synthetic EEG signal generation to simulate frequency bands, incorporate noise, and emulate specific phenomena. A Python-based tool allows controlled signal generation and export in various formats, and features a visualizer for analyzing user-uploaded signals in TXT, CSV, or EDF formats. EEGLAB evaluation confirms the accuracy and consistency of the generated signals. The tool supports research, diagnosis, and data analysis models requiring large datasets, particularly in neurological diseases like epilepsy. Pending web hosting, the tool will enhance accessibility and usability for broader applications.