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Effectively combining temporal projection noise suppression methods in magnetoencephalography.
Journal of Neuroscience Methods ( IF 3 ) Pub Date : 2020-05-19 , DOI: 10.1016/j.jneumeth.2020.108700
Maggie Clarke 1 , Eric Larson 1 , Kambiz Tavabi 1 , Samu Taulu 2
Affiliation  

BACKGROUND Magnetoencephalography (MEG) is an excellent non-invasive tool to study the brain. However, measurements often suffer from the contribution of external interference, including noise from the sensors. Suppression of noise from the data is critical for an accurate representation of brain signals. Due to MEG's limited spatial resolution and superior temporal resolution, noise suppression methods that operate in the temporal domain can be favorable. NEW METHOD We examined the independent and joint effects of two temporal projection noise suppression algorithms for MEG measurements: One commonly used algorithm which suppresses correlated noise; temporal signal space separation (tSSS) and one new method which suppresses uncorrelated sensor noise; oversampled temporal projection (OTP). RESULTS We found that both OTP and tSSS effectively suppress noise in raw MEG data and have the greatest effect of joint operation in cases where SNR is low, or when detecting higher SNR single-trial responses from raw data. We additionally demonstrate how the combination of OTP and tSSS is useful for the detectability of high-frequency brain oscillations (HFO). COMPARISON WITH EXISTING METHODS Although the mathematical description of OTP has been described before (Larson and Taulu, 2017), OTP's effect on HFOs in MEG data is novel. Additionally, the combination of OTP and commonly used temporal noise suppression algorithms (i.e., tSSS) has not been shown. CONCLUSIONS This finding is applicable to clinical populations such as epilepsy, where HFO signals are thought to be important markers for areas of seizure onset and are typically difficult to detect with non-invasive neuroimaging methods.

中文翻译:

在脑磁图中有效地结合时间投影噪声抑制方法。

背景技术脑磁图(MEG)是研究大脑的出色非侵入性工具。但是,测量常常受外部干扰的影响,包括来自传感器的噪声。数据中的噪声抑制对于准确表示大脑信号至关重要。由于MEG有限的空间分辨率和出色的时间分辨率,在时域中运行的噪声抑制方法可能是有利的。新方法我们研究了两种用于MEG测量的时间投影噪声抑制算法的独立和联合作用:一种常用的抑制相关噪声的算法;另一种用于抑制相关噪声的算法。时间信号空间分离(tSSS)和一种抑制不相关传感器噪声的新方法;过采样的时间投影(OTP)。结果我们发现,在SNR低或从原始数据中检测到较高的SNR单次试验响应时,OTP和tSSS都能有效地抑制原始MEG数据中的噪声,并且对联合操作具有最大的影响。我们还演示了OTP和tSSS的组合如何用于高频脑震荡(HFO)的可检测性。与现有方法的比较尽管之前已经描述了OTP的数学描述(Larson和Taulu,2017),但OTP对MEG数据中HFO的影响是新颖的。此外,尚未显示OTP与常用的时间噪声抑制算法(即tSSS)的组合。结论这一发现适用于诸如癫痫,
更新日期:2020-05-19
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