Atieh Schizophrenia EEG, a novel high-quality dataset designed to advance biomarker and machine learning research

Objective, quantifiable electroencephalogram (EEG) biomarkers and machine learning systems for schizophrenia (SZ) research are limited by the scarcity of large, diverse, and high-quality public datasets. To address this gap, we present ASEEG (Atieh Schizophrenia EEG), a resting-state EEG dataset designed to support methodological development and benchmarking in SZ studies. ASEEG comprises 198 recordings from 101 subjects, including 51 individuals diagnosed with SZ according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria and 50 demographically matched healthy controls. Data were collected at a clinical setting using a standardized 19-channel EEG montage (international 10–20 system) with a sampling rate of 500 Hz. For each subject, both eyes-open and eyes-closed resting-state conditions were recorded for five minutes each, enabling analysis of complementary neural dynamics. The dataset spans a wide age range (15–65 years) and provides balanced diagnostic groups and extended recording durations. ASEEG is intended for use in biomarker extraction, machine learning model development, validation, and comparative evaluation of EEG-based approaches for SZ research.