RT Journal Article
SR Electronic
T1 Deep Learning personalised, closed-loop Brain-Computer Interfaces for multi-way classification
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 256701
DO 10.1101/256701
A1 Pablo Ortega
A1 Cédric Colas
A1 Aldo Faisal
YR 2018
UL http://biorxiv.org/content/early/2018/01/30/256701.abstract
AB Brain-Computer Interfaces are communication systems that use brain signals as commands to a device. Despite being the only means by which severely paralysed people can interact with the world most effort is focused on improving and testing algorithms offline, not worrying about their validation in real life conditions. The Cybathlon’s BCI-race offers a unique opportunity to apply theory in real life conditions and fills the gap. We present here a Neural Network architecture for the 4-way classification paradigm of the BCI-race able to run in real-time. The procedure to find the architecture and best combination of mental commands best suiting this architecture for personalised used are also described. Using spectral power features and one layer convolutional plus one fully connected layer network we achieve a performance similar to that in literature for 4-way classification and prove that following our method we can obtain similar accuracies online and offline closing this well-known gap in BCI performances.