  State-of-the-art performance in electroencephalography (EEG) decoding tasks
is currently often achieved with either Deep-Learning (DL) or
Riemannian-Geometry-based decoders (RBDs). Recently, there is growing interest
in Deep Riemannian Networks (DRNs) possibly combining the advantages of both
previous classes of methods. However, there are still a range of topics where
additional insight is needed to pave the way for a more widespread application
of DRNs in EEG. These include architecture design questions such as network
size and end-to-end ability.How these factors affect model performance has not
been explored. Additionally, it is not clear how the data within these networks
is transformed, and whether this would correlate with traditional EEG decoding.
Our study aims to lay the groundwork in the area of these topics through the
analysis of DRNs for EEG with a wide range of hyperparameters. Networks were
tested on two public EEG datasets and compared with state-of-the-art ConvNets.
Here we propose end-to-end EEG SPDNet (EE(G)-SPDNet), and we show that this
wide, end-to-end DRN can outperform the ConvNets, and in doing so use
physiologically plausible frequency regions. We also show that the end-to-end
approach learns more complex filters than traditional band-pass filters
targeting the classical alpha, beta, and gamma frequency bands of the EEG, and
that performance can benefit from channel specific filtering approaches.
Additionally, architectural analysis revealed areas for further improvement due
to the possible loss of Riemannian specific information throughout the network.
Our study thus shows how to design and train DRNs to infer task-related
information from the raw EEG without the need of handcrafted filterbanks and
highlights the potential of end-to-end DRNs such as EE(G)-SPDNet for
high-performance EEG decoding.
