  We present a unified deep learning framework for the recognition of user
identity and the recognition of imagined actions, based on
electroencephalography (EEG) signals, for application as a brain-computer
interface. Our solution exploits a novel shifted subsampling preprocessing step
as a form of data augmentation, and a matrix representation to encode the
inherent local spatial relationships of multi-electrode EEG signals. The
resulting image-like data is then fed to a convolutional neural network to
process the local spatial dependencies, and eventually analyzed through a
bidirectional long-short term memory module to focus on temporal relationships.
Our solution is compared against several methods in the state of the art,
showing comparable or superior performance on different tasks. Specifically, we
achieve accuracy levels above 90% both for action and user classification
tasks. In terms of user identification, we reach 0.39% equal error rate in the
case of known users and gestures, and 6.16% in the more challenging case of
unknown users and gestures. Preliminary experiments are also conducted in order
to direct future works towards everyday applications relying on a reduced set
of EEG electrodes.
