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沉默间隔对使用非负矩阵因数分解提取人类频率后续反应的影响

Allison T Giordano¹, Fuh-Cherng Jeng¹, Taylor R Black¹

¹Communication Sciences and Disorders, Ohio University, Athens, Ohio, USA.

Perceptual and motor skills

|August 3, 2023

概括

排除静音间隔显著改善了使用源分离非负矩阵因子化 (SSNMF) 的频率跟踪响应 (FFR) 的提取. 这种优化增强了FFR检测并减少了噪音,有助于听觉处理研究.

科学领域:

神经科学是一个神经科学.
信号处理信号处理
听觉神经科学听觉神经科学

背景情况:

源分离非负矩阵因子化 (SSNMF) 是一种新的算法,用于从杂的头皮记录中提取频率跟踪响应 (FFR).
沉默间隔对SSNMF对FFR提取性能的影响尚不清楚.
FFR是评估听觉处理和神经可塑性的关键电生理学测量.

研究的目的:

调查包括或排除静音间隔对SSNMF算法在提取人类FFR中的性能的影响.
量化静音间隔对FFR提取效率和降噪的影响.

主要方法:

在23名正常听觉的成年人中,用一个具有上升频率轮的英语母音 /i/ 唤起FFR.
刺激的持续时间为150毫秒,刺激之间有150毫秒的静默间隔.
通过计算FFR增强和噪声残留来评估算法性能,并比较带有和没有静音间隔的条件 (WithSI vs. WithoutSI).

主要成果:

除了静音间隔 (WithoutSI),相比包括它们 (WithSI) (p < .05) 之外,导致明显更好的FFR增强和更低的噪音残留 (p < .05).
平均而言,不包括静音间隔,FFR增强率增加了11.78%,噪声残留量减少了20.69%.
这些发现表明,消除基于SSNMF的FFR提取的静音间隔具有量化效益.

结论:

静音间隔对SSNMF用于提取FFR的性能产生负面影响.
为优化SSNMF算法设计和改善FFR提取在听觉神经科学研究中,建议排除静默间隔.
这项研究提供了基于数据的建议,以加强SSNMF应用在分析电生理学听觉反应.

关键词:

算法的性能算法的性能.电脑脑电图 (EEG) 是一种电脑电图.接下来的频率响应响应.词汇语调的词汇语调.机器学习是机器学习.非负矩阵因子化的非负矩阵因子化.沉默的时间间隔.

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