在RSVP和SSVEP脑机接口任务期间使用多模态警觉 (MMV) 数据集
Wei Wei1, Kangning Wang1,2, Shuang Qiu3,4
1Chinese Academy of Sciences, Institute of Automation, Laboratory of Brain Atlas and Brain-inspired Intelligence, Beijing, 100190, China.
Scientific data
|August 10, 2024
概括
这项研究介绍了多模态警 (MMV) 数据集,其中包括来自脑计算机接口 (BCI) 任务的七个生理信号. 该MMV数据集有助于研究人员利用生理数据推进警估计.
科学领域:
- 神经科学和认知科学 神经科学和认知科学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 持续的注意力或警对于任务可靠性和性能至关重要.
- 现有的数据集可能无法完全捕捉到跨不同任务的警复杂性.
- 大脑计算机接口 (BCI) 研究需要强大的生理数据来进行性能分析.
研究的目的:
- 介绍和描述多式监控 (MMV) 数据集.
- 为警和生理信号分析研究提供全面的资源.
- 促进先进的警估计技术的发展.
主要方法:
- 获取七个生理信号:EEG,EOG,ECG,PPG,EDA,EMG,以及眼睛的运动.
- 在两个BCI任务中收集的数据:RSVP和SSVEP.
- 数据集分为原始,预处理,试验和特征数据阶段,为18个受试者提供了四个会议的数据集.
主要成果:
- 货币货币病毒数据集为警研究提供了丰富多彩的资源.
- 数据的结构支持在警估计算法中直接使用.
- 该数据集涵盖了与认知状态相关的广泛的生理信号.
结论:
- 该MMV数据集是对基于生理信号的警研究领域的宝贵贡献.
- 它允许灵活的重复使用,并满足BCI和认知监测的各种研究需求.
- 促进了理解和估计人类警的进步.
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