Graduation Year


Document Type




Degree Granting Department


Major Professor

Emanuel Donchin, Ph.D.

Committee Member

Cynthia Cimino, Ph.D.

Committee Member

David Drobes, Ph.D.

Committee Member

Mark Goldman, Ph.D.

Committee Member

Cathy McEvoy, Ph.D.

Committee Member

Douglas Nelson, Ph.D.


human-machine interface, ALS, assistive devices, event related potentials, locked-in syndrome


The current study evaluates the effectiveness of a Brain-Computer Interface (BCI) system that operates by detecting a P300 elicited by one of four randomly presented stimuli (i.e., YES, NO, PASS, END). Two groups of participants were tested. The first group included three ALS patients that varied in degree of disability, but all retained the ability to communicate; the second group included three Non-ALS controls. Each participant participated in ten experimental sessions during a period of approximately 6 weeks. Sessions were conducted either at the participant's home or in the lab. During each run the participant's task was to attend to one stimulus and disregard the other three. Stimuli were presented auditorily, visually, or in both modes. Additionally, on each run, the experimenter would either tell the participant which stimulus to focus on, or ask the participant a question and the participant would focus on the correct "YES/NO" answer to the question. Overall, for each participant, the ERPs elicited by the target stimuli could be discriminated from the non-target stimuli; however, less variability was observed in the Non-ALS group. Comparing across sessions, the within session variability was lower than across session variability. In addition, waveform morphology varied as a function of the presentation mode, but not in a similar pattern for each participant. Offline and simulated online classification algorithms conducted using step-wise discriminant analysis produced results suggesting the potential for online classification performance at levels acceptable for communication. Future investigations will begin to focus on testing online classification performance with real-time feedback, and continuing to examine stimulus properties to determine how to maximize P300 amplitude for individual users.