A Deep Classifier for Upper-Limbs Motor Anticipation Tasks in an Online BCI Setting

Bioengineering

Luca Ascari¹  Michele Barsotti¹  Andrea Valenti²  Davide Bacciu² 

[1] CAMLIN Italy s.r.l., 43121 Parma, Italy;Department of Computer Science, University of Pisa, 56127 Pisa, Italy;

关键词: deep learning;    brain–computer interfaces;    artificial neural networks;   

DOI  :  10.3390/bioengineering8020021

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown