Friday, April 26, 2024 10am to 11am
About this Event
Mohammad Naser
(Advisor: Dr. Sylvia Bhattacharya)
will propose a doctoral thesis entitled,
Utilizing Rest-EEG for Reduced User-Specific MI-EEG Training Data in BCI Applications
On
Friday, April 26th at 10:00 AM
Engineering Technology Center, Room Q104
Abstract
The use of individuals' Electroencephalography (EEG) signals is one of the rapidly emerging applications in the Brain-Computer Interaction (BCI) field. Specifically, Motor Imagery (MI) EEG signals, triggered by imagining physiological movements, have been a major focus for developing medical assistive technologies. In these applications, each imagined movement of a body part is mapped to a specific function of the assistive device. In all existing research, decoding the generated EEG signals to track the user's performed task is achieved using machine learning classifiers. However, these classifiers are constructed (trained) based on user-specific MI signals collected before building the computational model, acting as a barrier to the practical implementation of the MI paradigm. This project proposes a novel approach to alleviate the need for pre-use, user-specific MI training data by using a short resting-state EEG segment obtained in real-time from the user, combined with pre-trained classifiers. The project's contributions involve showcasing the value of Rest-EEG in understanding EEG generated by different mental commands and demonstrating the feasibility of a new line of thought to facilitate making the MI-EEG paradigm more practical, opening the door for broader implementation of human physiological signals in developing medical assistive and rehabilitation devices, such as wheelchairs and artificial limbs, promoting better living conditions for individuals with physical disabilities.
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