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

<p>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.</p>

Name thesis:degree_name

MA (Master of Arts)

Level thesis:degree_level

Thesis - unrestricted

Discipline thesis:degree_discipline

Psychology

Year dc:date.issued

2010

Author dc:creator

• Schwartz, Nicholas Edward

• Psychology
• Neuroscience
• ALS
• Brain-Computer Interface
• Computer Sciences
• Graphics and Human Computer Interfaces
• Physical Sciences and Mathematics

Statement dc:rights

• Copyright by the authors.

Repository record dc:identifier

https://dc.etsu.edu/etd/1775

OAI identifier oai:identifier

oai:dc.etsu.edu:etd-3130