Deep Learning and Knowledge Representation in Brain-Inspired Spiking Neural Networks for Brain-Computer Interfaces

Deep Learning and Knowledge Representation in Brain-Inspired Spiking Neural Networks for Brain-Computer Interfaces

Brain-Computer Interfaces aim at decoding neural commands from neurological signals and translate them into machine commands for manipulating digital devices. It provides a way of bypassing affected neural pathways in people with movement impairments. A growing body of literature on non-invasive Bra...

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Bibliographic Details
Main Author: Kumarasinghe, Kumara Vidanalage Dona Chithrangi Kaushalya (Author)
Other Authors: Kasabov, Nikola (Contributor), Taylor, Denise (Contributor)
Format: Others
Published: Auckland University of Technology, 2021-07-06T00:05:52Z.
Subjects:
Deep learning
Knowledge representation
Spiking Neural Networks
Brain-Computer Interface
Electroencephalography
Electromyography
Electrocorticography
Decode movements
Hand kinematics
Data stream mining
Neurotechnology
Neuroprosthetics
Non-invasive Brain-Computer Interfaces
Time-series
Spatio-temporal data
Machine learning
Brain data
Neurofeedback
Movement-related cortical potentials
Movement intention
Rehabilitation
Restorative Brain-Computer Interfaces
Brain-Inspired Brain-Computer Interfaces
Brain-Inspired Artificial Intelligence
Explainable Artificial Intelligence
Machine learning interpretability
NeuCube
Thesis
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