Date of Award
Santa Clara : Santa Clara University, 2021.
The intention of this project is to develop a brainwave classification system that will help restore the independence of those with severe motor function impairments. While current brain computer interface (BCI) technology offers a means of control for those with limited mobility, severely motor disabled individuals represent a population in need of methods to restore independent motor control. Thus, the objective of our project is to utilize neural signals from electroencephalogram (EEG) recordings to develop a machine learning classifier. Since our specific goal is to help those with limited mobility, we are focusing on motion imagery tasks which elicit a specific mu rhythm in the brain wave that occurs over the sensorimotor cortex. Using this principle, we can use EEG recordings of subjects imagining moving their limbs to extract particular features that can be used as motionless commands. The first stage of our project involves identifying a suitable motion imagery data set. This is followed by a pre-processing stage that involves filtering and transforming the signals. After performing necessary processing on our dataset, we train our machine learning model with the goal of developing a classification system in which test data sets can be entered and motion imagery command features can be automatically extracted and eventually utilized for the BCI.
Wang, Derrick and Lawler, Brendan, "Classifying Brainwaves for Brain-Computer Interface Technology" (2021). Bioengineering Senior Theses. 103.