  Neuroscientists and neuroengineers have long relied on multielectrode neural
recordings to study the brain. However, in a typical experiment, many factors
corrupt neural recordings from individual electrodes, including electrical
noise, movement artifacts, and faulty manufacturing. Currently, common practice
is to discard these corrupted recordings, reducing already limited data that is
difficult to collect. To address this challenge, we propose Deep Neural
Imputation (DNI), a framework to recover missing values from electrodes by
learning from data collected across spatial locations, days, and participants.
We explore our framework with a linear nearest-neighbor approach and two deep
generative autoencoders, demonstrating DNI's flexibility. One deep autoencoder
models participants individually, while the other extends this architecture to
model many participants jointly. We evaluate our models across 12 human
participants implanted with multielectrode intracranial electrocorticography
arrays; participants had no explicit task and behaved naturally across hundreds
of recording hours. We show that DNI recovers not only time series but also
frequency content, and further establish DNI's practical value by recovering
significant performance on a scientifically-relevant downstream neural decoding
task.

Machine Learning;Neural and Evolutionary Computing;Neurons and Cognition;Machine Learning