16 research outputs found
Deep Riemannian Networks for EEG Decoding
State-of-the-art performance in electroencephalography (EEG) decoding tasks
is currently often achieved with either Deep-Learning or
Riemannian-Geometry-based decoders. 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 as well as model training questions. 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.Comment: 26 pages, 15 Figure