Related Experiment Video
Updated: Aug 19, 2026

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
Published on: September 8, 2023
Changing the P300 brain computer interface
Jessica D Bayliss1, Samuel A Inverso, Aleksey Tentler
1Computer Science Department, Rochester Institute of Technology, Rochester, New York, USA. jdb@cs.rit.edu
Abstract:
Brain-computer interfaces (BCIs) are now feasible for use as an alternative control option for those with severe motor impairments. The P300 component of the evoked potential has proven useful as a control signal. Individuals do not need to be trained to produce the signal, and it is fairly stable and has a large evoked potential. Even with recent signal classification advances, on-line experiments with P300-based BCIs remain far from perfect. We present two potential methods for improving control accuracy. Experimental results in an evoked potential BCI, used to control items in a virtual apartment, show a reduced response exists when items are accidentally controlled. The presence of a P300-like signal in response to goal items means that it can be used for automatic error correction. Preliminary results from an interface experiment using three different button configurations for a yes/no BCI task show that the configuration of buttons may affect on-line signal classification. These results will be discussed in light of the special considerations needed when working with an amyotrophic lateral sclerosis (ALS) patient.

