A brain-computer interface (BCI) is an augmentative communication mechanism that does not rely on peripheral nerves or muscles. Current BCIs are error prone and slow with error rates of 10 to 30% and transmission rates of 10-25 bits/min, however, error recovery and correction in BCI has largely been neglected. The focus of this thesis is the development of a method to automatically recover errors in BCI using the P3 brain signal for response verification. The existence of the P3 signal in responses to controlled goal items is shown in an evoked potential BCI used to control items in a virtual apartment. A reduced response exists when items are accidentally controlled. Offline experiments were run, and with a theoretical mean improvement in accuracy from 78% to 85%, there was a statistically significant improvement (P < 0.008, Wilcoxon signed rank test) in accuracy of 3% using a correlation algorithm for P3 signal detection on responses. The presence of the P3 signal in responses to goal items indicates it can be used for automatic error recovery without requiring additional time, which will improve the speed and accuracy of brain-computer interfaces.

Library of Congress Subject Headings

Neural networks (Computer science); Electroencephalography--Data processing; Human-computer interaction; Computers and people with disabilities; Brain--Research; User interfaces (Computer systems)

Publication Date


Document Type


Student Type


Degree Name

Computer Science (MS)

Department, Program, or Center

Computer Science (GCCIS)


Jessica Bayliss


RIT – Main Campus

Plan Codes