P300-Based BCI Performance Prediction through Examination of Paradigm Manipulations and Principal Components Analysis.

• Main Author: Schwartz, Nicholas Edward
• Format: Others
• Published: Digital Commons @ East Tennessee State University 2010
• Subjects: Psychology; Neuroscience; ALS; Brain-Computer Interface; Computer Sciences; Graphics and Human Computer Interfaces; Physical Sciences and Mathematics
• Online Access: https://dc.etsu.edu/etd/1775; https://dc.etsu.edu/cgi/viewcontent.cgi?article=3130&context=etd

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.