Reference Signals In Intracranial Eeg: Implementation And Analysis

Abstract

The idea of an artifact-free brain activity recording has been circling around the scientific world for a few decades. Noise present in brain activity recordings may complicate the process of evaluation and interpretation. For the elimination of such unwanted components, the concept of virtual reference signals is usually used. In this work, the algorithms for reference signal estimation using common average-based method as well as more recent methods based on independent component analysis (ICA) were realized and evaluated on a new set of real clinical data. It was found that the ICA-based algorithms allow obtaining more accurate estimation of the reference signal as compared to the average-based one. Finally, all the methods were implemented into a free installable Python toolbox, which will be publicly available after additional testing on real data.