Brain-Inspired Spiking Neural Networks Enhance Continuous Decoding of Movements from Electroencephalography Signals and Enable Knowledge Representation and Transfer in Brain-Computer Interfaces
Introduction
Methodology
Experimental Validation
Conclusion and Future Work
Results and Discussion
Related Work
BI-BCI
How to represent knowledge
how to transfer knowledge
How to continuously decode movements
continous decoding
knowledge representation and transfer
decoding muscle synergies from EEG
Does the proposed model manifest learning in living nervous systems
incremental learning
evolving
adaptation
why it is important to develop methods that are inspired by how human brain process information for BCI
reasons
BCI - use brain data
BCI attempt to extract behaviour from neural activities