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Tackling Big Data in Human Intracranial Electroencephalography Recordings

2019; Lippincott Williams & Wilkins; Volume: 66; Issue: Supplement 1 Linguagem: Inglês

10.1093/neuros/nyz310_149

1524-4040

Patrick J. Karas, John F. Magnotti, Zhengjia Wang, Daniel Yoshor, Michael S. Beauchamp,

Neural dynamics and brain function

INTRODUCTION: Intracranial electrode recordings (ECoG [electrocorticography], sEEG [stereoelectroencephalography], and iEEG [intracranial electroencephalography]) are increasingly common across neurosurgery. Intracranial recordings during epilepsy monitoring, awake craniotomy, and deep brain stimulation are revolutionizing our understanding of basic brain function, providing access to direct recordings from human neurons. However analyzing this data is daunting. Hundreds of electrodes, each recording thousands of measurements each second, record for hours, days, or weeks. The resulting datasets easily reach hundreds of gigabytes in size, with trillions of datapoints representing a scale of data difficult to tackle. Presently, most labs write custom in-house code to analyze these datasets, making peer review of results next to impossible and exacerbating a reproducibility crisis. We present a software package, RAVE (R Analysis and Visualization of iEEG), equipped with dimension reduction techniques to allow users to more easily analyze giant iEEG datasets. METHODS: RAVE is written in R using the Shiny package, enabling it to run from any web browser. Data is notch-filtered then wavelet transformed to obtain power and phase components. Both common average and bipolar re-referencing are supported. Data is then temporally down-sampled, maintaining high frequency information but vastly reduces the size of datasets. Power and phase information is then interactively displayed across single or multiple electrodes. RESULTS: RAVE is a freely available software package with a growing user base, available at https://github.com/beauchamplab/rave/tree/master#rave. Inclusion of a preprocessing pipeline, signal decomposition, dimension reduction, and graphical display of iEEG data achieves our goals of standardizing data analysis, allowing users with limited programming background to analyze iEEG data, and increased transparency of published results. CONCLUSION: We have developed a dimension reduction, analysis, and visualization pipeline in RAVE that allows users to take large complex datasets and easily create publication quality images in a rigorous, transparent, and easily shareable way.