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基于 EMD 和 PCA 从 fMRI-EEG 同步记录中去除 BCG 伪影。

Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA.

机构信息

Center for Intelligent Signal and Imaging Research (CISIR) & Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak, Malaysia.

Center for Intelligent Signal and Imaging Research (CISIR) & Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak, Malaysia.

出版信息

J Neurosci Methods. 2017 Nov 1;291:150-165. doi: 10.1016/j.jneumeth.2017.08.020. Epub 2017 Aug 24.

Abstract

BACKGROUND

Simultaneous electroencephalography (EEG) and functional magnetic resonance image (fMRI) acquisitions provide better insight into brain dynamics. Some artefacts due to simultaneous acquisition pose a threat to the quality of the data. One such problematic artefact is the ballistocardiogram (BCG) artefact.

METHODS

We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact.

RESULTS

The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals.

COMPARISON WITH EXISTING METHODS

Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy.

CONCLUSIONS

The proposed method does not require any reference signal, prior information or assumption to extract the BCG artefact. It will be very useful in circumstances where the reference signal is not available.

摘要

背景

同时进行脑电图(EEG)和功能磁共振成像(fMRI)采集可以更好地了解大脑动态。由于同时采集而产生的一些伪影对数据质量构成威胁。其中一个有问题的伪影是心冲击图(BCG)伪影。

方法

我们开发了一种混合算法,该算法结合了经验模态分解(EMD)和主成分分析(PCA)的特征,以减少 BCG 伪影。该算法不需要额外的心电图(ECG)或眼电图(EOG)记录来提取 BCG 伪影。

结果

该方法在 11 名参与者的模拟和真实 EEG 数据上进行了测试。从模拟数据中,提取的 BCG 与模拟 BCG 之间的相似指数表明了该方法在去除 BCG 伪影方面的有效性。另一方面,使用两种条件记录真实数据,即静息状态(闭眼数据集)和任务影响(事件相关电位(ERP)数据集)。使用定性(视觉检查)和定量(相似指数、改进的归一化功率谱(INPS)比、功率谱、样本熵(SE))评估参数,评估结果表明,该方法可以有效地减少 BCG 伪影,同时保留神经元信号。

与现有方法的比较

与传统方法,即平均伪影减法(AAS)、最优基集(OBS)和联合独立成分分析和主成分分析(ICA-PCA)相比,结果的统计分析表明,该方法具有更好的性能,除了功率和样本熵外,所有定量参数的差异均有统计学意义。

结论

该方法不需要任何参考信号、先验信息或假设来提取 BCG 伪影。在参考信号不可用时,它将非常有用。

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