The PREP pipeline: standardized preprocessing for large-scale EEG analysis.
The PREP pipeline: standardized preprocessing for large-scale EEG analysis.
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DOI：
10.3389/fninf.2015.00016
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发表时间：
2015
影响因子：
3.5
通讯作者：
Robbins KA
中科院分区：
文献类型：
--
作者：
Bigdely-Shamlo N;Mullen T;Kothe C;Su KM;Robbins KA
收集脑成像和生理测量的技术已经变得便携和无处不在，为大规模分析真实世界的人类成像提供了可能。就其性质而言，此类数据庞大而复杂，因此必须进行自动化处理。本文展示了对脑电图预处理管道的早期阶段缺乏关注如何降低信噪比并将不必要的伪像引入数据，特别是在单精度计算中。我们证明了普通平均参考提高了信噪比，但噪声通道会污染结果。我们还证明了噪声信道的识别依赖于参考，并研究了滤波、噪声信道识别和参考的复杂相互作用。我们引入了一种多阶段鲁棒参考方案来处理有噪声的信道-参考交互。提出了一种标准化的早期脑电信号处理管道（PREP），并讨论了该管道在600多个脑电信号数据集上的应用。该管道包括为处理的每个数据集自动生成的报告。用户可以从http://eegstudy.org/prepcode下载PREP管道作为免费的MATLAB库。
The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode.
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DOI：
10.1109/embc.2013.6609968
发表时间：
2013
期刊：
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
影响因子：
--
作者：
Mullen T;Kothe C;Chi YM;Ojeda A;Kerth T;Makeig S;Cauwenberghs G;Jung TP
通讯作者：
Jung TP
影响因子：
3.4
作者：
Mitra, PP;Pesaran, B
通讯作者：
Pesaran, B
影响因子：
5.7
作者：
Chuang, Shang-Wen;Ko, Li-Wei;Lin, Chin-Teng
通讯作者：
Lin, Chin-Teng
影响因子：
5.7
作者：
Chuang, Chun-Hsiang;Ko, Li-Wei;Lin, Chin-Teng
通讯作者：
Lin, Chin-Teng
影响因子：
4.7
作者：
Hatz, F.;Hardmeier, M.;Fuhr, P.
通讯作者：
Fuhr, P.