4639248
doi
10.3390/bioengineering8020021
oai:zenodo.org:4639248
user-teaching-h2020
Michele Barsotti
CAMLIN Italy s.r.l.
Davide Bacciu
University of Pisa
Luca Ascari
CAMLIN Italy s.r.l.
A Deep Classifier for Upper-Limbs Motor Anticipation Tasks in an Online BCI Setting
Andrea Valenti
University of Pisa
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
<p>Decoding motor intentions from non-invasive brain activity monitoring is one of the most challenging aspects in the Brain Computer Interface (BCI) field. This is especially true in online settings, where classification must be performed in real-time, contextually with the user’s movements. In this work, we use a topology-preserving input representation, which is fed to a novel combination of 3D-convolutional and recurrent deep neural networks, capable of performing multi-class continual classification of subjects’ movement intentions. Our model is able to achieve a higher accuracy than a related state-of-the-art model from literature, despite being trained in a much more restrictive setting and using only a simple form of input signal preprocessing. The results suggest that deep learning models are well suited for deployment in challenging real-time BCI applications such as movement intention recognition.</p>
Zenodo
2021-03-26
info:eu-repo/semantics/article
4639247
user-teaching-h2020
1616762152.810232
701603
md5:2fb56fca7e89cebfc8461010c0517b7d
https://zenodo.org/records/4639248/files/bioengineering-08-00021-v3.pdf
public