Background

The methods available for pre-processing EEG data are rapidly evolving as researchers gain access to vast computational resources; however, the field currently lacks a set of standardized approaches for data characterization, efficient interactive quality control review procedures, and large-scale automated processing that is compatible with High Performance Computing (HPC) resources.

New Method

In this paper we describe an infrastructure for the development of standardized procedures for semi and fully automated pre-processing of EEG data. Our pipeline incorporates several methods to isolate cortical signal from noise, maintain maximal information from raw recordings and provide comprehensive quality control and data visualization. In addition, batch processing procedures are integrated to scale up analyses for processing hundreds or thousands of data sets using HPC clusters.

Results

We demonstrate here that by using the EEG Integrated Platform Lossless (EEG-IP-L) pipeline’s signal quality annotations, significant increase in data retention is achieved when applying subsequent post-processing ERP segment rejection procedures. Further, we demonstrate that the increase in data retention does not attenuate the ERP signal.

Conclusions

The EEG-IP-L state provides the infrastructure for an integrated platform that includes long-term data storage, minimal data manipulation and maximal signal retention, and flexibility in post processing strategies.

中文翻译：

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EEG集成平台无损（EEG-IP-L）预处理管道，用于客观信号质量评估，包括数据注释和盲源分离

背景

随着研究人员获得庞大的计算资源，用于预处理EEG数据的方法正在迅速发展。但是，该领域目前缺乏一套用于数据表征，有效的交互式质量控制审核程序以及与高性能计算（HPC）资源兼容的大规模自动化处理的标准化方法。

新方法

在本文中，我们描述了用于开发标准程序的基础结构，该程序用于半和全自动脑电数据的预处理。我们的管道采用多种方法将皮质信号与噪声隔离，从原始记录中获取最大信息，并提供全面的质量控制和数据可视化。此外，还集成了批处理程序，以扩大分析规模，以便使用HPC集群处理数百或数千个数据集。

结果

我们在此证明，通过使用EEG集成平台无损（EEG-IP-L）管道的信号质量注释，当应用后续的后处理ERP分段拒绝程序时，可以大大提高数据保留率。此外，我们证明数据保留的增加不会减弱ERP信号。

结论

EEG-IP-L状态为集成平台提供了基础架构，该平台包括长期数据存储，最少的数据处理和最大的信号保留以及后处理策略的灵活性。