A Multimodal Dataset Fusing Brain and Language Signals: EEG and Expressive Writing for Depression Research

This dataset presents a multimodal collection of resting-state EEG recordings and expressive writing samples from 55 Persian-speaking university students, designed to support research in depression assessment and affective computing. Each participant completed two validated depression questionnaires (BDI-II and PHQ-9), four open-ended writing tasks in Persian, and a resting-state EEG session using an 8-channel dry-electrode headset (Unicorn Hybrid Black). The EEG data includes both eyes-open and eyes-closed conditions, sampled at 250 Hz. Writing samples are provided in both Persian (original) and English (translated), covering introspective and emotionally charged prompts. Depression severity labels were derived from a fused score combining BDI-II and PHQ-9 responses. All data are anonymized and organized for reproducibility, including raw EEG files (CSV and EDF), structured text documents, participant metadata, and baseline machine learning code for unimodal and multimodal analysis. This dataset enables cross-disciplinary research at the intersection of neuroscience, psychology, and language modeling, particularly within underrepresented cultural contexts.