  The brain computer interface (BCI) is a nonstimulatory direct and
occasionally bidirectional communication link between the brain and a computer
or an external device. Classically, EEG-based BCI algorithms have relied on
models such as support vector machines and linear discriminant analysis or
multiclass common spatial patterns. During the last decade, however, more
sophisticated machine learning architectures, such as convolutional neural
networks, recurrent neural networks, long short-term memory networks and gated
recurrent unit networks, have been extensively used to enhance discriminability
in multiclass BCI tasks. Additionally, preprocessing and denoising of EEG
signals has always been key in the successful decoding of brain activity, and
the determination of an optimal and standardized EEG preprocessing activity is
an active area of research. In this paper, we present a deep neural network
architecture specifically engineered to a) provide state-of-the-art performance
in multiclass motor imagery classification and b) remain robust to
preprocessing to enable real-time processing of raw data as it streams from EEG
and BCI equipment. It is based on the intertwined use of time-distributed fully
connected (tdFC) and space-distributed 1D temporal convolutional layers
(sdConv) and explicitly addresses the possibility that interaction of spatial
and temporal features of the EEG signal occurs at all levels of complexity.
Numerical experiments demonstrate that our architecture provides superior
performance compared baselines based on a combination of 3D convolutions and
recurrent neural networks in a six-class motor imagery network, with a
subjectwise accuracy that reaches 99%. Importantly, these results remain
unchanged when minimal or extensive preprocessing is applied, possibly paving
the way for a more transversal and real-time use of deep learning architectures
in EEG classification.
