P300-Based BCI Performance Prediction through Examination of Paradigm Manipulations and Principal Components Analysis.
MA (Master of Arts)
Date of Award
Committee Chair or Co-Chairs
Eric W. Sellers
Russell W. Brown, Stacey L. Williams
Severe neuromuscular disorders can produce locked-in syndrome (LIS), a loss of nearly all voluntary muscle control. A brain-computer interface (BCI) using the P300 event-related potential provides communication that does not depend on neuromuscular activity and can be useful for those with LIS. Currently, there is no way of determining the effectiveness of P300-based BCIs without testing a person's performance multiple times. Additionally, P300 responses in BCI tasks may not resemble the typical P300 response. I sought to clarify the relationship between the P300 response and BCI task parameters and examine the possibility of a predictive relationship between traditional oddball tasks and BCI performance. Both waveform and component analysis have revealed several task-dependent aspects of brain activity that show significant correlation with the user's performance. These components may provide a fast and reliable metric to indicate whether the BCI system will work for a given individual.
Thesis - unrestricted
Schwartz, Nicholas Edward, "P300-Based BCI Performance Prediction through Examination of Paradigm Manipulations and Principal Components Analysis." (2010). Electronic Theses and Dissertations. Paper 1775. https://dc.etsu.edu/etd/1775
Copyright by the authors.