在功能近红外光谱信号中的生理噪声过使用波形变换和长期短期记忆网络.
So-Hyeon Yoo1, Guanghao Huang2, Keum-Shik Hong1,2
1School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Republic of Korea.
Bioengineering (Basel, Switzerland)
|June 28, 2023
概括
这项研究引入了一种新的功能近红外光谱 (fNIRS) 方法,用于在没有已知的试验期的情况下识别大脑活动. 它有效地预测和消除任务会话期间的生理噪声,改进大脑-计算机接口应用程序.
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:
- 功能近红外光谱 (fNIRS) 通常通过从已知的试验期获得的所需血液动力学响应函数 (dHRF) 来识别激活的大脑通道.
- 当试验期未知时,这种常规方法不适用,这对实时脑电脑接口构成了挑战.
- 估计大脑信号的开始时间对于被动大脑-计算机接口至关重要.
研究的目的:
- 提出一种创新的fNIRS方法来识别激活道,而不依赖于预定义的试验期或dHRF.
- 开发一种技术来预测和减去任务期间的生理噪声,即使是未知的试验期.
- 为fNIRS分析提供替代解决方案,当传统方法由于噪声干扰而无法应用时.
主要方法:
- 使用最大重叠离散波束转换 (MODWT) 来从静止状态的fNIRS数据中提取波动信号.
- 识别与生理噪声相关的低频波段,并使用长短期记忆 (LSTM) 网络来训练它们.
- 将静止状态数据分解为九个小波,使用第五到第九个用于学习和预测,并将信号从静止状态扩展到任务状态,以保持相位信息.
主要成果:
- 提出的方法成功地预测和减去任务期间的生理噪声,而不需要dHRF.
- 第八波段分解显示了在15秒的窗口内,dHRF和没有dHRF的方法之间的最大预测误差差.
- 这种技术为fNIRS分析提供了可行的替代方案,当生理噪音使激活期检测复杂化时.
结论:
- 开发的方法为未知试验期的场景中fNIRS信号分析提供了可靠的方法.
- 它有效地解决了生理噪声的挑战,特别是当其频率与神经激活信号重叠时.
- 这一创新具有很大的潜力,可以通过实现精确的脑信号检测来推进被动脑计算机接口应用.
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