  Electrode "pop" artifacts originate from the spontaneous loss of connectivity
between a surface and an electrode. Electroencephalography (EEG) uses a dense
array of electrodes, hence "popped" segments are among the most pervasive type
of artifact seen during the collection of EEG data. In many cases, the
continuity of EEG data is critical for downstream applications (e.g. brain
machine interface) and requires that popped segments be accurately
interpolated. In this paper we frame the interpolation problem as a
self-learning task using a deep encoder-decoder network. We compare our
approach against contemporary interpolation methods on a publicly available EEG
data set. Our approach exhibited a minimum of ~15% improvement over
contemporary approaches when tested on subjects and tasks not used during model
training. We demonstrate how our model's performance can be enhanced further on
novel subjects and tasks using transfer learning. All code and data associated
with this study is open-source to enable ease of extension and practical use.
To our knowledge, this work is the first solution to the EEG interpolation
problem that uses deep learning.
