Supporting data for "Dual-Alpha: A Large EEG Study for Dual-Frequency SSVEP Brain-Computer Interface"

The domain of brain-computer interface (BCI) technology has experienced significant expansion in recent years. However, the field continues to face a pivotal challenge due to the dearth of high-quality datasets. This lack of robust datasets serves as a bottleneck, constraining the progression of algorithmic innovations and, by extension, the maturation of the BCI field. <br>This study details the acquisition and compilation of electroencephalogram (EEG) data across three distinct dual-frequency steady-state visual evoked potential (SSVEP) paradigms, encompassing over one hundred participants. Each experimental condition featured 40 individual targets with 5 repetitions per target, culminating in a comprehensive dataset consisting of 21,000 trials of dual-frequency SSVEP recordings. We performed an exhaustive validation of the dataset through signal-to-noise ratio (SNR) analyses and Task-related Component Analysis (TRCA), thereby substantiating its reliability and effectiveness for classification tasks. <br>The extensive dataset presented is set to be a catalyst for the accelerated development of BCI technologies. Its significance extends beyond the BCI sphere and holds considerable promise for propelling research in psychology and neuroscience. The dataset is particularly invaluable for discerning the complex dynamics of binocular visual resource distribution.脑机接口（Brain-Computer Interface，BCI）技术领域近年来取得了长足发展。然而，该领域仍面临一项关键挑战：高质量数据集的匮乏。这类鲁棒性强的数据集缺失已成为制约算法创新进展，进而阻碍脑机接口领域成熟发展的瓶颈。 本研究详细阐述了三种不同双频稳态视觉诱发电位（Steady-State Visual Evoked Potential，SSVEP）范式下脑电图（Electroencephalogram，EEG）数据的采集与整理工作，共纳入百余名受试者。每个实验条件设置40个独立刺激靶点，每个靶点重复呈现5次，最终形成包含21000次双频SSVEP记录的综合数据集。我们通过信噪比（Signal-to-Noise Ratio，SNR）分析与任务相关成分分析（Task-related Component Analysis，TRCA）对该数据集进行了全面验证，证实其在分类任务中具备可靠性与有效性。 本研究发布的大规模数据集有望成为推动脑机接口技术加速发展的催化剂。其研究价值不仅局限于脑机接口领域，同时对推动心理学与神经科学领域的研究亦具有重要意义。该数据集对于解析双眼视觉资源分配的复杂动态机制尤为宝贵。