Multi-Factor SSVEP EEG Dataset

This dataset contains electroencephalography (EEG) recordings collected in a multi-factor steady-state visual evoked potential (SSVEP) brain\u2013computer interface (BCI) experiment. Fifteen healthy adults (10 males, 5 females; 20 \u00b1 2 years), all with normal color vision and no history of neurological or psychiatric disorders, participated after providing written informed consent. Visual stimuli followed a full-factorial design crossing three attributes: color (red, orange, white), shape (circle, square), and spacing ratio between stimulus size and center-to-center distance (1:1, 2:1, 10:1), yielding 18 stimulus conditions. Four flickering targets were displayed simultaneously on a 60 Hz LCD monitor at 10.25, 11.25, 12.25, and 13.25 Hz. Each condition contained 10 blocks, and each block comprised four 9-s trials (1.5-s cue, 6-s stimulation, 1.5-s rest), resulting in 40 trials per condition and 720 trials per subject. EEG was acquired with a DSI-24 wireless dry-electrode system (24 channels; 19 scalp electrodes plus 2 ear references and 3 auxiliary channels; 10\u201320 montage; 300 Hz sampling rate). Each block file provides a continuous 36-s EEG segment (4 consecutive trials) with channel labels. The data are organized by subject, stimulus condition, and block index.This resource enables systematic evaluation of stimulus design and decoding algorithms in SSVEP-BCI. It is particularly suitable for benchmarking classical machine-learning methods and modern deep neural networks, for studying multi-factor stimulus effects (shape, color, spacing), and for developing robust SSVEP decoding, transfer learning, and data-augmentation strategies.