Faster than Thought Error Detection Using Machine Learning to Detect Errors in Brain Computer Interfaces

This research thesis seeks to use machine learning on data from invasive brain-computer interfaces (BCIs) in rhesus macaques to predict their state of movement during center-out tasks. Our research team breaks down movements into discrete states and analyzes the data using Linear Discriminant Analysis (LDA). We find that a simplified model that ignores the biological systems unpinning it can still detect the discrete state changes with a high degree of accuracy. Furthermore, when we account for underlying systems, our model achieved high levels of accuracy at speeds that ought to be imperceptible to the primate brain.