一个全面的数据集用于家用电器控制,使用基于ERP的BCI,并应用跨学科转移学习
Jongmin Lee1, Minju Kim1, Dojin Heo1
1Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea.
Frontiers in human neuroscience
|February 16, 2024
概括
这项研究引入了用于使用脑电图 (EEG) 和事件相关潜能 (ERP) 控制家用电器的大脑计算机接口 (BCI) 的新数据集. 转移学习显著改善了跨不同用户和环境的BCI性能.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 人与计算机的交互
背景情况:
- 大脑-计算机接口 (BCI) 通过直接将大脑活动与计算机系统联系起来,提供革命性的人机交互.
- 使用脑电图 (EEG) 和事件相关潜能 (ERP) 的非侵入性BCI显示出对家用电器控制等实际应用的希望.
研究的目的:
- 提供基于ERP的在线BCI的综合数据集,用于在不同的环境中控制各种家用电器.
- 用转移学习来解决ERP中的跨学科变异性,以提高BCI通用性.
主要方法:
- 通过液晶显示器和增强现实 (AR) 来控制家电 (电视,门锁,灯,扬声器,空调) 的84个受试者的在线BCI数据.
- 采用了两种转移学习方法:"范式内"用于在相同的刺激呈现中进行概括",跨范式"用于将模型扩展到不同的环境中.
主要成果:
- 转移学习有效地提高了基于ERP的BCI的通用性.
- 证明了跨不同学科和各种刺激呈现环境的转移学习的成功应用.
结论:
- 开发的数据集和转移学习方法显著提高了基于ERP的BCI的稳定性和适用性,用于现实世界家电控制.
- 这项工作为更加个性化和适应性的BCI系统铺平了道路.
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