eldBETA: A Large Eldercare-oriented Benchmark Database of SSVEP-BCI for the Aging Population

Global population aging poses an unprecedented challenge and calls for a rising effort in eldercare and healthcare. Steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) boasts its high transfer rate and shows great promise in real-world applications to support aging. Public database is critically important for designing the SSVEP-BCI systems. However, the SSVEP-BCI database tailored for the elder is scarce in existing studies. Therefore, in this study, we present a large <strong>eld</strong>ercare-oriented <strong>BE</strong>nchmark database of SSVEP-BCI for <strong>T</strong>he <strong>A</strong>ging population (eldBETA). The eldBETA database consisted of the 64-channel electroencephalogram (EEG) from 100 elder subjects, each of whom performed seven blocks of 9-target SSVEP-BCI task. We expect that the eldBETA database would provide a substrate for the design and optimization of the BCI systems intended for the elders.

全球人口老龄化正面临前所未有的挑战，亟需在养老护理与医疗卫生领域加大投入。稳态视觉诱发电位脑机接口（Steady-state visual evoked potential based brain-computer interface，SSVEP-BCI）具有极高的信息传输速率，在支撑老龄化社会发展的实际应用中展现出巨大潜力。公开数据集是研发SSVEP-BCI系统的关键基础，然而现有研究中针对老年群体定制的SSVEP-BCI公开数据集仍十分匮乏。因此，本研究构建了一款面向养老护理的大型老年群体SSVEP-BCI基准数据集eldBETA。该数据集包含100名老年受试者的64通道脑电图（Electroencephalogram，EEG）数据，每位受试者均完成了7组包含9个目标的SSVEP-BCI任务。我们期望该eldBETA数据集能够为面向老年群体的脑机接口系统的设计与优化提供核心支撑。