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