伪在线BCI评估框架:使用各种MI和SSVEP数据集的MOABB视角
Igor Carrara1, Theodore Papadopoulo1
1Université Côte d'Azur (UCA), INRIA, Cronos Team, Nice, France.
Journal of neural engineering
|December 19, 2023
概括
大脑计算机接口 (BCI) 研究经常使用离线分析,这可能会导致结果偏差. 这项研究引入了一种伪在线BCI分析方法,以更好地反映现实世界的性能,并改进算法评估.
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
- 生物医学工程 生物医学工程
背景情况:
- 大脑计算机接口 (BCI) 对辅助技术至关重要,可以在线,离线和伪线上运行.
- 离线BCI分析虽然准确,但通过处理整个数据集引入了偏差,与实时应用程序不同.
- 现有的BCI框架主要支持离线分析,限制在更现实的场景中对算法的评估.
研究的目的:
- 扩展MOABB框架用于伪在线BCI分析,使算法的直接比较成为可能.
- 引入一个"置状态"事件,用于在伪在线模式下进行全面的数据处理.
- 弥合线下BCI研究和现实世界绩效评估之间的差距.
主要方法:
- 在MOABB框架内实施重叠的滑动窗口,用于伪在线数据处理.
- 包含一个"置状态"事件,以捕获与任务无关的数据变化.
- 使用规范化的马修斯相关系数和信息传输速率验证算法性能.
主要成果:
- 15年来对运动图像和稳定状态视觉唤起的潜在数据集进行最先进的BCI算法的分析.
- 在线和伪在线BCI分析方法之间的性能差异的统计证明.
- 通过传统的线下BCI处理方法引入的偏差的识别.
结论:
- 开发的伪在线BCI分析方法提供了对分类算法的更现实的评估.
- 在线和伪在线模式中实现比较分析,提高了BCI研究结果的可靠性.
- 这一进步将导致科学界对BCI算法性能进行更准确,更全面的报告.
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