通过使用专门设计的信心估计器来提高情感大脑计算机接口的可靠性.
IEEE journal of biomedical and health informatics
|August 1, 2025
概括
这项研究引入了一种新的算法,用于估计基于脑电图 (EEG) 的情感大脑计算机接口 (aBCI) 的可靠性. 该算法提供实时的信任分数,增强对aBCI应用程序的信任和安全性.
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:
- 使用脑电图 (EEG) 的情感大脑计算机接口 (aBCI) 有多种应用.
- 由于噪音和生理变异,aBCI的性能可能会不可预测地下降,阻碍信任.
- 实时可靠性估计对于安全可靠的aBCI部署至关重要.
研究的目的:
- 开发和验证用于估计aBCI实时可靠性的算法.
- 为提供反映aBCI当前识别能力的概率可信度评分.
- 提高aBCI在现实场景中的可信度和适用性.
主要方法:
- 从EEG识别网络中利用最大软max概率 (MSP) 作为置信度得分.
- 雇佣了缩放和投影操作员,以校准MSP并减轻噪音和主题变化带来的偏差.
- 从理论强度的最大入原理推导出可靠性估计器.
主要成果:
- 与SEED和SEED-IV数据集的基准相比,拟议的算法在估计aBCI可靠性方面表现优异.
- 该算法显示了可称赞的适应新对象的适应性.
- 理论上证实,可靠性估计器不会影响BCI的业绩.
结论:
- 开发的算法有效地估计了aBCI的可靠性,提供实时的信心得分.
- 这种方法提高了aBCI的可信度,为更广泛的应用铺平了道路.
- 该方法为基于EEG的aBCI的性能下降的挑战提供了可靠的解决方案.
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