Electroencephalogram signal enhancement using Nonnegative Matrix Factorization for BCI applications

The electroencephalogram (EEG) reflects the neural electrical activity and used in brain-computer interface (BCI) systems which provide assistive technologies for people with various disabilities. The proposed research is mainly focused to apply Nonnegative matrix factorization for EEG signal enhancement for BCI applications to mitigate the limitations of existing BCI system such as slow speeds, limitation of hardware requirement and power dissipation and susceptibility to artifacts. The finding of the proposed research will contribute greatly to the benefit of the society considering that brain-computer interface used in various fields such as in advanced healthcare, neuroergonomics and smart environment, educational and self-regulation, security, and authentication fields. The expected outcome of the proposed project would be to deliver a sophisticated algorithm to process and analyze EEG data which can improve the performance of the existing BCI systems.

Faculty Supervisor:

Sridhar Krishnan

Student:

Partner:

Universidade Federal do Espírito Santo

Discipline:

Engineering

Sector:

University:

Toronto Metropolitan University

Program:

Globalink Research Award