Reduction of noise from magnetoencephalography data
Reduction of noise from magnetoencephalography data
复制标题
减少脑磁图数据的噪声
DOI：
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发表时间：
2005
影响因子：
3.2
通讯作者：
S. Honda
中科院分区：
文献类型：
--
作者：
S. Okawa;S. Honda
提出了一种脑磁图（MEG）数据去噪方法。该方法是卡尔曼滤波和因子分析相结合。利用脑磁测量中的正问题，建立了卡尔曼滤波器的状态空间模型。因子分析提供卡尔曼滤波器所需的噪声协方差的估计，以消除独立的加性传感器噪声。该方法支持独立分量分析（伊卡），这是很难使用的MEG分析，由于传感器噪声。数值实验研究了该方法的性能。在最大信噪比（SNR）为-10 dB的单偶极子情况下，近似等于原始MEG数据，成功地从噪声数据中估计出无噪声信号;观察到峰值潜伏期延迟0.02 s，峰值振幅衰减15-40%。此外，在一个多偶极子的情况下，独立组件预处理与所提出的方法具有很高的相关性，0.88在最低的，与0.69和0.52的那些预处理与传统的带通滤波器。结果表明，该降噪方法有效地降低了传感器噪声.通过该方法可以得到高信噪比的独立分量。还演示了真实的MEG数据分析。该方法从非平均单次试验数据中提取听觉诱发反应。
A noise reduction method for magnetoencephalography (MEG) data is proposed. The method is a combination of Kalman filtering and factor analysis. A statespace model for a Kalman filter was constructed using the forward problem in MEG measurement. Factor analysis provide estimations of noise covariances required by the Kalman filter to eliminate independent additive sensor noise. The proposed method supports independent component analysis (ICA), which is difficult to use in MEG analysis owing to the sensor noise. Numerical experiments were conducted to investigate the performance of the proposed method. In a single dipole case where the maximum signal-to-noise ratio (SNR) was — 10 dB, approximately equivalent to raw MEG data, noise-free signals were successfully estimated from noisy data; a 0.02 s delay of the peak latency and 15–40% of attenuation of the peak amplitude were observed. Moreover, in a multiple dipole case, independent components preprocessed with the proposed method had high correlation, 0.88 at the lowest, with correlation of 0.69 and 0.52 for those preprocessed with conventional bandpass filters. The results show that the noise reduction method reduces sensor noise effectively. High SNR-independent components are obtained by the proposed method. Real MEG data analysis was also demonstrated. The proposed method extracted auditory evoked responses from unaveraged single-trial data.
影响因子：
3.5
作者：
SARVAS, J
通讯作者：
SARVAS, J