The PREP pipeline: standardized preprocessing for large-scale EEG analysis

Abstract

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.

Precuneus Failures in Subjects of the PSEN1 E280A Family at Risk of Developing Alzheimer’s Disease Detected Using Quantitative Electroencephalography

“…The preprocessing approach used improves the test-retest reliability [40] and includes power line interference removal, detection and interpolation of noisy channels, and computing of a robust average reference, using the PREP pipeline [41]. For each subject, a maximum of 5% of the channels were replaced by interpolation.…”

Automated rejection and repair of bad trials in MEG/EEG

“…Compared to other methods (e.g., PREP [7]), we do not make the assumption that sensors must be globally bad. In fact, it can detect and repair sensors even when they are locally bad, thus saving data.…”

“…We compared our proposed algorithm to the RANSAC implementation in the PREP pipeline [7]. RANSAC being an algorithm robust to outliers, serves as a competitive benchmark.…”

“…For single-trial analysis, an outlier detection algorithm is better suited. The Random Sample Consensus (RANSAC) algorithm implemented as part of the PREP pipeline [7], for example, annotates outlier sensors as bad.…”

EEG signal enhancement using cascaded S-Golay filter

“…Various researchers have implemented digital filters for smoothing purpose and significant improvement is observed in the results [14,15]. If EEG pre-processing is not performed carefully then undesired noise may be introduced which lowers the signal to noise ratio [16]. Savitzky and Golay presented a new smoothing technology [17] in which generalized MAF upholds the higher frequency contents.…”