Chinese Academy of Sciences, Institute of Automation, Laboratory of Brain Atlas and Brain-inspired Intelligence, Beijing, 100190, China.
Tianjin University, Academy of Medical Engineering and Translational Medicine, Tianjin, 300072, China.
Sci Data. 2024 Aug 10;11(1):867. doi: 10.1038/s41597-024-03729-8.
Vigilance represents an ability to sustain prolonged attention and plays a crucial role in ensuring the reliability and optimal performance of various tasks. In this report, we describe a MultiModal Vigilance (MMV) dataset comprising seven physiological signals acquired during two Brain-Computer Interface (BCI) tasks. The BCI tasks encompass a rapid serial visual presentation (RSVP)-based target image retrieval task and a steady-state visual evoked potential (SSVEP)-based cursor-control task. The MMV dataset includes four sessions of seven physiological signals for 18 subjects, which encompasses electroencephalogram(EEG), electrooculogram (EOG), electrocardiogram (ECG), photoplethysmogram (PPG), electrodermal activity (EDA), electromyogram (EMG), and eye movement. The MMV dataset provides data from four stages: 1) raw data, 2) pre-processed data, 3) trial data, and 4) feature data that can be directly used for vigilance estimation. We believe this dataset will achieve flexible reuse and meet the various needs of researchers. And this dataset will greatly contribute to advancing research on physiological signal-based vigilance research and estimation.
警觉性代表着持续集中注意力的能力，在确保各种任务的可靠性和最佳性能方面起着至关重要的作用。在本报告中，我们描述了一个多模态警觉性（MMV）数据集，该数据集包含在两个脑机接口（BCI）任务中采集的七种生理信号。BCI 任务包括基于快速序列视觉呈现（RSVP）的目标图像检索任务和基于稳态视觉诱发电位（SSVEP）的光标控制任务。MMV 数据集包含 18 名受试者的四个会话的七种生理信号，包括脑电图（EEG）、眼电图（EOG）、心电图（ECG）、光体积描记图（PPG）、皮肤电活动（EDA）、肌电图（EMG）和眼球运动。MMV 数据集提供了四个阶段的数据：1）原始数据，2）预处理数据，3）试验数据，以及 4）可直接用于警觉性估计的特征数据。我们相信，该数据集将实现灵活的重复使用，并满足研究人员的各种需求。并且该数据集将极大地促进基于生理信号的警觉性研究和估计的研究。
