  Machine learning can extract information from neural recordings, e.g.,
surface EEG, ECoG and {\mu}ECoG, and therefore plays an important role in many
research and clinical applications. Deep learning with artificial neural
networks has recently seen increasing attention as a new approach in brain
signal decoding. Here, we apply a deep learning approach using convolutional
neural networks to {\mu}ECoG data obtained with a wireless, chronically
implanted system in an ovine animal model. Regularized linear discriminant
analysis (rLDA), a filter bank component spatial pattern (FBCSP) algorithm and
convolutional neural networks (ConvNets) were applied to auditory evoked
responses captured by {\mu}ECoG. We show that compared with rLDA and FBCSP,
significantly higher decoding accuracy can be obtained by ConvNets trained in
an end-to-end manner, i.e., without any predefined signal features. Deep
learning thus proves a promising technique for {\mu}ECoG-based brain-machine
interfacing applications.
