功能性近红外光谱电机执行和图像的数据收集,增强和分类
Baiwei Sun1, Xiu Zhang1,2, Xin Zhang1,2
1Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, China.
The Review of scientific instruments
|March 4, 2025
概括
这项研究引入了一种使用功能近红外光谱学 (fNIRS) 的新方法,用于准确识别脑计算机接口 (BCI) 的运动图像. SE-KAN模型实现了高精度,克服了传统EEG方法的局限性.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 信号处理 信号处理
背景情况:
- 运动图像识别对于大脑与计算机接口 (BCI) 是至关重要的.
- 电脑电图 (EEG) 被运动干扰所限制.
- 功能近红外光谱学 (fNIRS) 提供了一个替代方案,但公开数据有限.
研究的目的:
- 开发一种使用fNIRS数据识别运动执行和图像的新方法.
- 通过数据增强技术来应对有限的fNIRS数据的挑战.
- 提高BCI系统的准确性和可靠性.
主要方法:
- 使用可穿戴fNIRS设备设计了一项运动执行和图像实验.
- 为fNIRS信号识别提出了一个修改后的Kolmogorov-Arnold网络 (SE-KAN).
- 由于受试者数量很少,使用了Wasserstein生成对抗网络来增强数据.
主要成果:
- 通过SE-KAN方法实现了高单个对象准确度 (96.36 ± 2.43%).
- 跨主题的准确性达到了84.72±3.27%.
- 证明了SE-KAN在为BCI任务分类fNIRS信号方面的有效性.
结论:
- 开发的SE-KAN方法和数据集推进了基于fNIRS的BCI研究.
- 这种方法为准确的运动图像识别提供了一个有希望的解决方案.
- 这些发现有助于开发更强大,更耐干扰的BCI系统.
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